Data Scientist вакансии

Рассматриваем и сильных выпускников с заметными достижениями - олимпиады.

Задачи: — Полное владение датасетами безопасности: сбор, очистка, разметка, контроль качества, версионирование и подготовка к обучению и оценке моделей. — Перевод требований политики безопасности в четкие и последовательные критерии разметки и оценки, в тесном сотрудничестве со специалистами по политике. — Проектирование и управление процессами разметки, включая поиск, адаптацию и контроль внешних участников для обеспечения высокого качества. — Создание рабочих процессов оценки моделей в продакшене с использованием реальных данных для отслеживания производительности и выявления проблем. — Разработка легковесных пайплайнов и инструментов на Python и SQL для ускорения и воспроизводимости работы с данными, в партнерстве с ML-инженерами по требованиям к "готовности к обучению". Требования: — Опыт работы Data Scientist или аналогичной роли с реальными датасетами и ML-моделями. — Глубокое понимание качественного датасета: сбор, очистка, выборка, разметка, контроль качества, оценка. — Уверенное владение Python и SQL для анализа и практических рабочих процессов с данными. — Сильное критическое мышление и способность принимать решения в условиях неоднозначности, умение превращать сложные руководства в последовательные и масштабируемые решения. — Автономность и хорошие коммуникативные навыки, способность работать в междисциплинарной команде (политика, инженерия, исследования) в быстро меняющейся среде. Преимущества: — Опыт в AI safety, trust & safety, модерации контента или обнаружении мошенничества/злоупотреблений. — Опыт создания датасетов или фреймворков оценки, опыт с adversarial/safety-ориентированным ML. — Опыт управления процессами разметки с участием человека, версионирования датасетов или инструментов ML-пайплайнов. — Опыт внедрения руководств совместно с командами политики или юридическими отделами. Условия: — Работа в гибридном формате в Лондоне, тесное взаимодействие с командами AI Safety и Policy. — Равные возможности для всех кандидатов без дискриминации по различным признакам.

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD This role owns how Higgsfield measures its product - from the first sign-up to whether people come back. You are the measurement owner for onboarding, first generation, the paywall, plans and credits, and retention. Product managers ship fast here - your job is to make sure they ship knowing. You make sure: - Every product decision has a number attached to it before it ships and after it ships - Experiments are designed to be readable, and then read honestly - including when the answer is “this didn’t work” - The events the product emits can be trusted; you own the definition, not just the query - A PM gets an answer in hours, not next sprint You are the person who decides what “it worked” means. WHAT YOU WILL DO Product measurement - Own the core product metrics: activation, first-generation success, generation depth, free→paid conversion, repeat usage, retention by cohort and segment. - Define each metric once, write the definition down, and hold the line on it - one definition across dashboards, decks, and Slack. - Instrument new features before launch, with product and engineering: which events, which properties, what success looks like, when we call it. - Watch the health of the event stream itself - tracking drift, double counting, missing parameters, naming breakage and find the problem before a decision gets made on top of it. Experimentation - Design A/B tests properly: hypothesis, one primary metric, unit of randomization, MDE, sample size, run length, guardrail metrics. - Read them honestly: significance, peeking, novelty effects, sample-ratio mismatch, segment heterogeneity. - Separate “we proved this works,” “we proved this doesn’t,” and “we can’t tell from this test” - and say which one out loud. Product & monetization analysis - In-product funnel work: onboarding and quiz, first generation, paywall, checkout, plan choice, credit top-ups. - Pricing and packaging analysis: plan mix, credit consumption, unit economics per generation, margin by feature and by model. - Feature adoption and its real effect on retention and revenue — separating “users who do X retain better” from “making people do X improves retention.” - Behavioural segmentation: casual creators vs corporate/B2B, new vs returning, by model and by use case. Mixing them hides everything that matters. Making it usable - Build the few dashboards PMs actually open on their own — decision-shaped, not comprehensive. - Write short readouts a non-analyst can act on. Visualize data for humans, not for other analysts. - Automate recurring reporting so your hours go to new questions, not refreshes. - Work in SQL, Python, BigQuery and product analytics tools. We use AI heavily - resourcefulness beats syntax. WHO WE'RE LOOKING FOR We're looking for an analyst with opinions. You should have: - Strong SQL: window functions, cohorts, funnels and retention on raw event data, without help. - Python at working level for analysis (pandas, notebooks). Entry level is fine - we use AI heavily. - Real experimentation experience: you have designed tests, run them, and killed features with the results. - Statistical honesty: you know what a p-value does and does not entitle you to say, and you have refused to call a flat test a win. - Understanding of subscription + one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, deferred value of unspent credits. - The instinct to check the instrument before explaining the movement - a surprising number is a claim, not a fact. - Ownership: you decide what's worth analyzing, you chase the fix, you follow the recommendation to a shipped change. - Clear written and spoken English, B2+. Backgrounds that often do well: - Product analysts from consumer subscription or PLG products - Growth/BI analysts who moved into product and stayed - Early-startup analysts who built product measurement from zero - Data scientists who got tired of models and want decisions WHAT THIS ROLE IS NOT This role is not a fit if you: - Wait for a ticket to tell you what to analyze - Want to build models more than you want to change decisions - Need a data engineering team and clean tables to exist before you can start - Would report a lift you don't believe in because a stakeholder wants it - Need perfect data before you can say anything useful - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - Take-home with real-shaped data (7 days, ~10–12 hours of work) - Team interview (60 min) - Paid on-site trial (1 month) WHAT WE OFFER - Competitive base salary in USD, based on your experience, skills, and the scope of the role. - Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth. - Relocation support to Almaty for candidates moving from another city or country. - A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company. - Opportunities for professional growth, ownership, and career development as the company scales. - Company-provided equipment, meals, transportation, or other office benefits.

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.   WHAT THIS ROLE MEANS AT HIGGSFIELD Right now we treat 25M+ people as one user. The same paywall, the same discount, the same lifecycle email, the same retention effort. And we mostly conclude that a campaign worked by looking at what the people we sent it to did next — which is not the same thing. This role makes the company act per user, and know what that action was worth. You own both halves of that: the models that decide who we aim at, and the causal reads that say whether it landed. Who is about to leave. Who will come back on their own without us spending anything. Whose decision a discount actually changes. And what a payer is worth over their life, once the discount, the refunds and the compute they consume are all inside the number. You make sure: - Discounts and credit vouchers go to the people whose behaviour they change, and nowhere else - We know who is about to churn early enough to do something about it — and we know whether the something worked - Lifetime value is a number we can defend by cohort, by plan and by the discount a cohort was acquired on, not one blended average that hides all three - Every model and every campaign has a holdout behind it, so we can always state what it is worth in money WHAT YOU WILL DO Churn, retention & lifecycle propensity - Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for one-time credit-pack buyers — two different machines, not one model with a flag on it. - Know the difference between a model that predicts churn and a model that reduces it. AUC earned by detecting users who already stopped using the product is worth nothing. - Hunt leakage relentlessly. Cancellation-adjacent features will hand you a beautiful offline number and a useless system. - Pair every score with an intervention and a holdout. The deliverable is a retained user, not a ranked list. Uplift modelling & offer targeting - Own incrementality on everything we aim at a user: discounts, credit vouchers, trials, upgrade prompts, win-back campaigns, retention saves. - Model uplift, not propensity.Targeting the users most likely to convert spends margin on people who were going to convert anyway; the entire value of this work is finding the users whose decision the offer changes. - Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently — including the ones marketing will ask you to switch off. - Respect the guardrails. An offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find the abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup. - Work with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets — you should want that conversation, not route around it. Lifetime value & payer economics - Own LTV: cohort-based, censored honestly, stated on contribution margin rather than gross revenue, and split by plan, segment and acquisition discount. - Model the parts that actually move it — refunds and chargebacks, credit breakage and expiry, plan migration and downgrades, and the compute an unlimited plan consumes. - Make it a decision input, not a slide: what we can pay for a user, which discount depth pays back, which plan we should stop selling. - Keep it current and versioned. A stale LTV curve is worse than no curve, because people act on it. Experimentation & causal measurement - Design and read the experiments behind pricing, packaging, offers and lifecycle — with the product analytics team, not in parallel to it. - Reach for the right tool when a clean experiment is impossible: difference-in-differences, matching, switchback and geo tests, synthetic control — and name the assumption each one is buying. - Separate "users who do X retain better" from "making users do X improves retention." Every time, out loud. - Kill your own results. A finding that doesn't survive a second look should die at your hands, not in a board deck. How we ship - Own the work end to end: framing the decision, features, training, evaluation, the scored audience landing in the tool that sends the offer, monitoring, retraining. - Models ship as batch scores and policy rules into lifecycle tooling, the paywall and pricing — plus the readout that says what they earned. There is no real-time serving stack to babysit. - Monitor for drift. New model launches, price changes, discount campaigns and seasonality all move the ground under a deployed score. - Write down what each model is worth, in money, and keep that number current. WHO WE'RE LOOKING FOR - Experience shipping models that changed what the business did to real users— a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this. - Depth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale. - Genuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift. - Strong SQL and Python: cohorts, funnels and retention on raw event data without help; gradient boosting; and the boring parts — feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot. - Understanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer. - Commercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model. - Pragmatism. A segment rule shipped this month that lifts retention beats a two-quarter uplift platform. Then you replace the rule. - Clear written and spoken English, B2+. Backgrounds that often do well: - Growth, retention or monetization data scientists from subscription, gaming, fintech or e-commerce — anywhere offers and churn are modelled with real money attached - CRM and lifecycle data scientists who owned campaign targetingandits incrementality - Causal inference and uplift specialists who ship policies rather than papers - Strong product analysts who moved into modelling and kept the decision instinct WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to train or fine-tune generative video models — that's what our R&D ML Engineer roles do, and they're open - Want to own a recommender or a serving stack — ranking is not in this role's scope today - Would target an offer at the users most likely to buy and call it personalization - Report a lift from a before-and-after comparison, or from comparing the users you targeted to everyone you didn't - Optimize AUC and hand the score to someone else to work out what to do with it - Need clean labelled data, a feature store and an experimentation platform to exist before you can start - Want predictable 9–5 workdays HIRING PROCESS We move fast: - Screening call (30 min) - Technical interview & case study (1 hour) - Practical home task (7 days) - Team interview (60 min) - Paid on-site trial (1 month) WHAT WE OFFER - Competitive base salary in USD, based on your experience, skills, and the scope of the role. - Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth. - Relocation support to Almaty for candidates moving from another city or country. - A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company. - Opportunities for professional growth, ownership, and career development as the company scales. - Company-provided equipment, meals, transportation, or other office benefits.

Banco Plata is a digital bank and fintech startup specializing in consumer lending, credit cards with a cashback program, and interest-free installment plans. Remote work or relocation options available to Cyprus, Serbia, Spain, Georgia, or Mexico.

Plata is a Mexican fintech startup specializing in consumer lending through the Plata Card. Remote work. Flexible working hours. Relocation support (Serbia, Georgia, Mexico or Colombia).

Location: San Francisco Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We’re hiring a Data Scientist to help build and scale our internal analytics capabilities. This is a foundational role where you’ll create dashboards, models and insights to power business and product teams alike. You’ll collect requirements, define key metrics, and deliver recommendations directly to stakeholders. You'll define what success looks like across a technical, usage-based platform and turn ambiguous questions into analyses, forecasts, and experiments that shape Baseten’s product and strategy. RESPONSIBILITIES - Build and maintain production-grade models and dashboards across multiple functions with a focus on accuracy, simplicity and user experience. - Define and instrument core metrics around capacity, availability, ROI, product adoption, revenue and costs. - Ingest and transform raw data using tools like dbt, Airbyte, and Databricks. - Partner with Engineering, Finance, Marketing, and Sales teams to understand goals and translate them into data solutions - Analyze product, operational and commercial data to identify opportunities, quantify tradeoffs, and influence product and GTM priorities. - Present findings and recommendations to technical and nontechnical audiences, including company leaders. REQUIREMENTS - 5+ years of experience in analytics, BI, or data engineering roles - Strong SQL skills with the ability to write complex queries for data transformation and analysis - Experience designing medallion data architectures, including raw, conformed, and business-ready models with testing, documentation, and lineage. - Excellent communication skills and a collaborative approach to working with cross-functional teams - Demonstrated ability to explain complex work and make clear recommendations to technical and business audiences. NICE TO HAVE - Experience with AI infrastructure, developer platforms, or inference. - Hands-on forecasting expertise, including ARIMA, Prophet, or comparable time-series methods, with disciplined backtesting, error analysis, and scenario planning. - Working knowledge of model-serving and training workflows and the infrastructure metrics that shape performance and cost. - Practical experimentation experience, including test design, power analysis and knowing when directional evidence is sufficient to act. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: United Kingdom ClinChoice is a global full-service CRO committed to professional growth and a quality-driven “one-team” culture. We are seeking a Principal Statistician to join our in-house team and support projects across a diverse client portfolio, including pharmaceutical, biotechnology, medical device, and consumer health companies of varying sizes. We are seeking a highly skilled Principal Statistician to lead statistical activities for clinical trials. This role requires hands-on statistical expertise, strong communication skills, and the ability to independently lead projects while collaborating closely with cross-functional teams. Join our team: you can be part of making a difference in peoples' lives and experience a fulfilling and rewarding career! Key Responsibilities Lead and provide statistical support for clinical studies. Act as the lead statistician on assigned studies, overseeing statistical strategy and deliverables Author and review key statistical documents, including: Statistical Analysis Plans (SAPs) Analysis Database Specifications (ADS) Statistical sections of clinical study reports Review and interpret analysis outputs, ensuring accuracy, quality, and regulatory compliance Collaborate with cross-functional teams including Clinical, Programming, Data Management, and Regulatory Provide guidance and oversight to statistical programmers as needed Communicate statistical concepts, results, and risks clearly to both technical and non-technical stakeholders Ensure adherence to regulatory standards (FDA, ICH, GCP) and company SOPs Required Qualifications MS or PhD in Statistics, Biostatistics, or a related field There are no specific therapeutic area requirements; however, experience in Oncology, Rare Disease, or Neuroscience would be advantageous. Strong oral and written communication skills, with the ability to effectively communicate internally and with clients. Demonstrated understanding and insight in statistics, drug development process, and relevant FDA regulations. Pharmaceutical, CRO, or related industry experience with clinical trials, including interaction with Regulatory Agencies, especially the FDA. The Application Process Once you have submitted your CV, you will receive an acknowledgement that we received it. If you have the requirements we need, you will be invited for phone interview as the first step.  Unfortunately, due to the number of applications we receive, we cannot reply to everyone individually if you are not successful. Who will you be working for? About ClinChoice ClinChoice is a global full-service CRO specializing in clinical development and functional solutions for pharmaceutical, biotechnology, medical device, and consumer health companies. We have over 28 years of proven high-quality delivery and results across all our services, with over 4,000 professionals in more than 20 countries across the Americas, Europe, and Asia-Pacific. Our Company Ethos Our mission drives our culture: to contribute to a healthier and safer world by accelerating the development and commercialization of innovative drugs and devices. Our employees are the most valuable company asset and they are the fulcrum around which all ClinChoice activities are built and close management and training is the core instrument to develop and maintain highly-qualified personnel. The continuous training keeps the resources qualified in terms of competence and expertise and gives all personnel the clear tools needed to manage both internal and client processes with the same methodology.   The success of these core values is evidenced by our below industry average turnover rates. ClinChoice is an equal opportunity employer. We have based our success on attracting, developing, and promoting talent guided by diversity and inclusivity. Our employees come from very diverse backgrounds: gender, race, beliefs, and ethnicities. We recognize this is our strength and celebrate it. #LI-TT1 #LI-Remote #LI-Principal #Permanent

Location: United Kingdom ClinChoice is a global full-service CRO committed to professional growth and a quality-driven “one-team” culture. We are seeking a Senior Statistician to join our in-house team and support projects across a diverse client portfolio, including pharmaceutical, biotechnology, medical device, and consumer health companies of varying sizes. We are seeking a highly skilled Senior Statistician to to join our biometrics team. This is a full-time permanent opportunity with REMOTE. . Join our team: you can be part of making a difference in peoples' lives and experience a fulfilling and rewarding career! Job Responsibilities Responsible for protocol development including study design, sample size calculation, randomization, and statistical analysis plan for assigned studies. Provide statistical oversight to studies and assure adequate quality and consistency with project requirements. Responsible for assuring that data for statistical analyses are complete, accurate, and consistent. Responsible for statistical analysis plans and the accuracy and timeliness of statistical input into reports or decisions. Responsible for the validity of the analysis and exploring alternative analysis strategies as needed. Demonstrates extensive understanding of statistical concepts and methodologies. Recognizes and corrects flaws in scientific reasoning and statistical interpretation. Responsible for accuracy and consistency of statistical tables, figures, and data listings, accuracy of report text, and consistency between summary tables in the body of reports and the corresponding source tables and listings. Responsible for the statistical methods section of the reports. Identifies and corrects common flaws in the interpretation of results, inconsistency in presentation or inference, adherence to the report guidelines, and ensures project-wide consistency. Effectively mentor peers with regard to statistical methodology and provide appropriate training to less experienced statisticians. Manage activities of statisticians across projects by appropriately coordinating assignments and reviewing work so that projects are delivered on time with high quality. Requirements: MS or PhD in Statistics, Biostatistics, or related field.  PhD with 3-4 years of experience or MS with 5-7 years of experience. There are no specific therapeutic area requirements; however, experience in Oncology, Rare Disease, or Neuroscience would be advantageous. Strong oral and written communication skills, with the ability to effectively communicate internally and with clients. Demonstrated understanding and insight in statistics, drug development process, and relevant FDA regulations. Pharmaceutical, CRO, or related industry experience with clinical trials, including interaction with Regulatory Agencies, especially the FDA. The Application Process Once you have submitted your CV, you will receive an acknowledgment that we received it. If you have the requirements we need, you will be invited for a phone interview as the first step.  Unfortunately, due to the number of applications we receive, we cannot reply to everyone individually if you are not successful. Who will you be working for? About ClinChoice ClinChoice is a global full-service CRO specializing in clinical development and functional solutions for pharmaceutical, biotechnology, medical device, and consumer health companies. We have over 28 years of proven high-quality delivery and results across all our services, with over 4,000 professionals in more than 20 countries across the Americas, Europe, and Asia-Pacific. Our Company Ethos Our mission drives our culture: to contribute to a healthier and safer world by accelerating the development and commercialization of innovative drugs and devices. Our employees are the most valuable company asset and they are the fulcrum around which all ClinChoice activities are built and close management and training is the core instrument to develop and maintain highly-qualified personnel. The continuous training keeps the resources qualified in terms of competence and expertise and gives all personnel the clear tools needed to manage both internal and client processes with the same methodology.   The success of these core values is evidenced by our below-industry-average turnover rates. ClinChoice is an equal-opportunity employer. We have based our success on attracting, developing, and promoting talent guided by diversity and inclusivity. Our employees come from very diverse backgrounds: gender, race, beliefs, and ethnicities. We recognize this is our strength and celebrate it. #LI-TT1 #LI-Remote #Senior#permanent

Location: United States ClinChoice is a global full-service CRO committed to professional growth and a quality-driven “one-team” culture. We are seeking a Principal Statistician to join our in-house team and support projects across a diverse client portfolio, including pharmaceutical, biotechnology, medical device, and consumer health companies of varying sizes. We are seeking a highly skilled Principal Statistician to lead statistical activities for clinical trials. This role requires hands-on statistical expertise, strong communication skills, and the ability to independently lead projects while collaborating closely with cross-functional teams. Join our team: you can be part of making a difference in peoples' lives and experience a fulfilling and rewarding career! Key Responsibilities Lead and provide statistical support for clinical studies, primarily within Oncology Act as the lead statistician on assigned studies, overseeing statistical strategy and deliverables Author and review key statistical documents, including: Statistical Analysis Plans (SAPs) Analysis Database Specifications (ADS) Statistical sections of clinical study reports Review and interpret analysis outputs, ensuring accuracy, quality, and regulatory compliance Collaborate with cross-functional teams including Clinical, Programming, Data Management, and Regulatory Provide guidance and oversight to statistical programmers as needed Communicate statistical concepts, results, and risks clearly to both technical and non-technical stakeholders Ensure adherence to regulatory standards (FDA, ICH, GCP) and company SOPs Required Qualifications MS or PhD in Statistics, Biostatistics, or a related field Strong experience in Oncology studies Hands-on experience writing: Statistical Analysis Plans (SAPs) Analysis Database Specifications Reviewing and interpreting analysis results SAS programming skills required (hands-on or strong working knowledge) Excellent written and verbal communication skills Experience working in a CRO and/or pharmaceutical/biotech environment Preferred Qualifications Experience leading Phase II–III clinical trials Prior interaction with regulatory agencies Experience mentoring junior statisticians or programmers The Application Process Once you have submitted your CV, you will receive an acknowledgement that we received it. If you have the requirements we need, you will be invited for phone interview as the first step.  Unfortunately, due to the number of applications we receive, we cannot reply to everyone individually if you are not successful. Who will you be working for? About ClinChoice ClinChoice is a global full-service CRO specializing in clinical development and functional solutions for pharmaceutical, biotechnology, medical device, and consumer health companies. We have over 28 years of proven high-quality delivery and results across all our services, with over 4,000 professionals in more than 20 countries across the Americas, Europe, and Asia-Pacific. Our Company Ethos Our mission drives our culture: to contribute to a healthier and safer world by accelerating the development and commercialization of innovative drugs and devices. Our employees are the most valuable company asset and they are the fulcrum around which all ClinChoice activities are built and close management and training is the core instrument to develop and maintain highly-qualified personnel. The continuous training keeps the resources qualified in terms of competence and expertise and gives all personnel the clear tools needed to manage both internal and client processes with the same methodology.   The success of these core values is evidenced by our below industry average turnover rates. ClinChoice is an equal opportunity employer. We have based our success on attracting, developing, and promoting talent guided by diversity and inclusivity. Our employees come from very diverse backgrounds: gender, race, beliefs, and ethnicities. We recognize this is our strength and celebrate it. #LI-TT1 #LI-Remote #LI-Principal #Permanent

Location: San Francisco, California Workplace: Hybrid WHO WE ARE Notion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. ABOUT THE ROLE: - You will apply your expertise in statistical inference and experimentation to understand the impact of product changes and drive decision-making. - You will explore ambiguous areas and conduct analysis to identify opportunities for the Growth org, helping shape the strategy and roadmap. - You'll partner closely with product, engineering, and marketing leaders at every step of the development process, driving forward data science and analytics work. - You'll work with your teams to determine north star and operational metrics, and build the foundational datasets and dashboards to monitor progress and make key decisions. - You'll influence the broader company by communicating your findings and driving change in our product and business. (Insights are useful. Impact is even better!) SKILLS YOU'LL NEED TO BRING: - You are comfortable with ambiguity and motivated by business results. - You have expertise in SQL and at least one scripting language (ideally Python or R). - You know how to use statistical inference and experimentation to drive actionable recommendations. - You are comfortable transforming raw data to build your own data sets if the measure you need doesn't exist yet. - You have a bias for using the right tools to get a job done with maximum efficiency. You have experience making tradeoffs between speed and accuracy. NICE TO HAVES: - You have worked at a fast-growing start-up. - You have experience at a B2B SaaS company. - You have a track record of acting as a thought partner to product, engineering, and marketing leaders, driving strategic decisions on these teams using data. - You have experience building predictive models, and you know how to evaluate their effectiveness. We hire talented and passionate people from a variety of backgrounds because we want our global employee base to represent the wide diversity of our customers. If you’re excited about a role but your past experience doesn’t align perfectly with every bullet point listed in the job description, we still encourage you to apply. If you’re a builder at heart, share our company values, and enthusiastic about making software toolmaking ubiquitous, we want to hear from you. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know. Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $145,000 – $233,000 per year. By clicking “Submit Application”, I understand and agree that Notion and its affiliates and subsidiaries will collect and process my information in accordance with Notion’s Global Recruiting Privacy Policy https://notion.notion.site/Notion-Global-Recruiting-Privacy-Policy-fc3eb4e829354a26a2bb6fd5e289b550. https://dev.notion.so/a3ce416456924276a59de91bca740028?pvs=21 #LI-Onsite A NOTE ON AI You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement — when that’s the case, we'll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on. EQUAL OPPORTUNITY & ACCOMMODATIONS We hire talented people from a wide range of backgrounds. If you’re excited about this role but don’t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.

Location: United Kingdom; Spain; Germany Workplace: remote Help Us Build The Future of Travel At Airalo, we're making it easier for people to stay connected wherever they travel. As the world's first eSIM store, we help millions of travelers access affordable mobile data in 200+ countries and regions around the world. Today, we're a team of 400+ people across 60+ countries, building a product used by travelers every day. We've grown quickly, but we've worked hard to keep what matters: trust, ownership, and the freedom for people to do great work without unnecessary layers or bureaucracy. We're fully remote by design, genuinely global, and united by a shared mission to make travel simpler for everyone. Your Next Destination Location: Remote. Contract: Full-time, permanent. Benefits: Learn more about our benefits here in this link - https://airalo-public.notion.site/Benefits-25396a97ffca81fb9bc1f0be479f1be3?pvs=74  Languages: English is our main working language day to day, so you'll need to be comfortable communicating in it both in meetings and async.   We're looking for a Staff Data Scientist, Pricing to build the analytical and machine learning capability that pricing runs on, from demand modelling through to the systems that recommend price. You won't be starting from zero. We've invested in the foundational data and infrastructure that pricing decisions depend on. What's missing is the capability on top: models the business trusts, experiments that settle pricing questions with evidence, and economics defined once so they hold consistently across Commercial, Product and the customer experience. The arc of the role runs from measurement to prediction to prescription - understanding how demand responds to price, forecasting how it will respond, and ultimately building the models that recommend the price itself. What You'll Do: Build and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment. Develop machine learning models for demand forecasting and willingness-to-pay, and take them from exploration through to production with the monitoring and retraining that keeps them honest. Move us from predictive to prescriptive by building the optimisation layer that turns forecasts and elasticities into recommended prices under margin, competitive and partner constraints. Design and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible. Define pricing within our data ecosystem, owning the governed definitions of price, cost and package economics that reporting, analysis and product surfaces all read from. Partner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit. Set the analytical standard for pricing at Airalo, from what counts as evidence through to how a model or recommendation gets validated before it influences live pricing. What You'll Bring: 7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions. Experience with dynamic or algorithmic pricing systems in production. An advanced degree in Econometrics, Statistics, Operations Research or similar, or equivalent applied depth in demand estimation and the identification problems that make naive price-quantity regressions wrong. Hands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment Familiarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches. Proven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes. Experience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly. Strong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses. The ability to move comfortably between financial, product and operational data and connect the analysis to a financial outcome. Genuine partnership instincts, translating between analytical rigour and commercial reality so that Commercial and Product come to you early rather than after the decision. Fluency in the Python ML stack alongside strong SQL, with the engineering hygiene to hand over code that others can run and maintain. Excellent communication skills, including the ability to make a methodological argument to people who won't check your standard errors. A self-starter mindset that thrives in ambiguity and brings structure without waiting for permission. Comfort using AI tools to augment analytical work, with a point of view on where they help and where they don't Nice to have: Experience in marketplace, telecom, travel, or subscription/usage-based businesses. Bayesian or hierarchical modelling for sparse segments and long-tail SKUs. Competitive price response modelling, or working with scraped competitor pricing data. Familiarity with dbt, LightDash, or similar semantic/BI layers. Experience designing semantic or metric layers consumed by both analytics and production systems. Experience working in cross-functional teams spanning commercial, finance and product disciplines. If you are interested in this position, please apply via the link. Why People Join Us We started Airalo to make staying connected effortless, wherever you are in the world. Today, millions of travelers rely on our eSIMs, and many of the people building the product were customers first. Our team spans 60+ countries, united by a shared belief that great work happens when people are trusted to own what they do. There's trust to own what you do, space to focus without constant layers of process, and a clear sense of what you're responsible for from day one. As Airalo continues to grow, so do the opportunities to learn, take on new challenges, and make a real impact - from anywhere in the world. Before You Hit Apply By applying, you acknowledge and agree that, in case of successful application, Airalo may request to run background checks as a condition for entering into an agreement with you. Rest assured that these checks will only occur upon your prior consent and at the end of the selection process, and will be strictly limited to what is allowed under the laws that are applicable to you. All data that you share or that we collect in connection with such checks will be processed in accordance with our Privacy Policy, available here: www.airalo.com/more-info/privacy-policy?srsltid=AfmBOooBT0rXAj1FaNelZ3VfN0wvhwzvAoxdtHnOKSVETpiSjiXVuycy    We sincerely thank all applicants in advance for submitting their interest in this opportunity. Airalo is an equal-opportunity employer and values diversity, equity & inclusion. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations upon request for individuals with disabilities throughout our job interview process. At Airalo, we believe in roaming-free recruitment. Just as we provide clear connectivity for travelers, we provide clear salary ranges for our candidates. 🌍 This role is remote and open to candidates across our supported locations in Europe. The range below reflects the full salary range for this role across the countries where it’s available: 📍UK: £93,200 - £139,800 📍Spain: €77,600- €116,400 📍Western/Northern Europe (Germany, France, Austria, Denmark, Sweden, Finland, Norway): €66,400 - €166,800 📍Southern Europe (Portugal, Italy, Greece, Malta): €70,000 - €137,000) 📍Central & Eastern Europe (Czechia, Hungary, Poland, Lithuania, Estonia): €60,000 - €117,000 📍South-Eastern Europe / Balkans (Romania, Serbia, Bulgaria): €56,000 – €100,000 Your Home Destination: 🛫  Local market rates and cost of living vary. Your specific range within this band depends on where you’re based, and there’s room to discuss what’s right for the person and the role. We’ll walk through your range together on your first call with our Talent team so we’re aligned for takeoff! 🎒✨

Задачи: — Решать сложности бизнес-заказчиков и оценивать полученный результат. — Планировать работу команды (Data Scientists, ML-engineers). — Проектировать развитие системы прогнозирования спроса. — Проектировать с руководителями смежных технических команд совместные интеграции. — Программировать и проводить code review. Требования: — Опыт работы в ML и DS проектах от 5 лет. — Опыт решения задач прогнозирования временных рядов. — Управленческий опыт работы. — Умение писать на Python. — Умение работать с данными на PySpark, SQL или Pandas. — Знание и понимание классических ML алгоритмов (линейная регрессия, деревья решений и градиентный бустинг). — Знание Deep Learning фреймворков (Pytorch, Transformers).

Задачи: - разрабатывать надежные backend-сервисы для AI-помощников на основе GigaChat - создавать и поддерживать системы сбора и обработки данных: парсинг, логирование, аналитика - оптимизировать код, повышать стабильность и производительность сервисов под высокой нагрузкой - применять инструменты Data Science для улучшения работы систем и интеграции LLM в реальные бизнес-процессы. Требования: - рассматриваем студентов ВУЗа технической специальности, бакалавр 3-4 курс, магистратура 1-2 курс - уверенно владеете Python - знаете библиотеки FastAPI, aiohttp, SQLAlchemy, pandas, bs4 - имеете навыки асинхронного программирования - разбираетесь в SQL и ORM - понимаете принципы REST API, а также основы объектно-ориентированного проектирования (ООП, SOLID) - работаете с Git.

Location: New York, NY (HQ); San Francisco, CA Workplace: Hybrid ABOUT RAMP Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $275B in annualized spend flows in and out of 85,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 7.5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. WHAT YOU’LL DO - Full stack development, building models to consume, transform, and expose data to stakeholders and production systems - Drive a culture of experimental design, testing agenda, and best practices - Contribute to the culture of Ramp’s data team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way - Collaborate with Finance teams (e.g. GTM Finance, StratFin) to develop financial insights and influence business decisions - Work closely with data engineering teams to capture, move, store, and transform raw data into highly actionable insights, and partner with business teams to turn those insights into action WHAT YOU NEED - Minimum of 3 years of industry experience in Data Science / Software Engineering / Finance - Strong AI proficiency as a lever to quickly adopt new skills and subject matter - Track record of shipping high quality products and features at scale - Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions - Familiarity with financial metrics and processes (e.g. contribution profit, financial statements, monthly close) and / or B2B enterprise sales cycle metrics and processes - Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery) and how to write efficient SQL queries NICE-TO-HAVES - Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Hightouch or equivalents) - Familiarity with BI tools (preferably Looker, Omni, Sigma, Hex or equivalent) and experience distributing data insights via reports and dashboards - Strong intuition on business strategy and customer empathy (ROI, growth channels, sales cycles, customer incentives) - Strong perspective on analytics engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development) - Experience partnering with finance teams or within the payments and financial technology space BENEFITS AVAILABLE TO ALL FULL-TIME RAMP EMPLOYEES (GLOBAL) - Flexible PTO - Centralized home-office equipment ordering - Health and wellness stipend - Budget for intra-office travel - Weekly coffee stipend UNITED STATES - 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents - One Medical annual membership - 401(k), including employer match on contributions made while employed by Ramp - Fertility HRA (up to $10,000 per year) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay - Pet insurance - In-office perks: lunch, snacks, drinks, and more - Relocation expense coverage to NYC or SF (if needed) CANADA - Group medical, dental, and vision coverage through Sun Life - Life, AD&D, and disability coverage - Fertility drug coverage (up to $4,000 lifetime) - Group Retirement Plan with employer match (RRSP + DPSP) - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay - Employee Assistance Program and virtual care through Lumino Health UNITED KINGDOM - Private medical insurance through Freedom Elite - Virtual GP and at-home care via eMed x Livi - Workplace pension through Penfold, with salary sacrifice option - Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.   OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.   Beware of recruiting scams: Ramp will only contact you through official @Ramp.com http://Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.   Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice

RELOCATION TO GERMANY adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we’ve become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually. Architecting Intelligence to Optimize 200M+ Daily Decisions As the intelligence core of our engineering organization, our Data Science team doesn’t just deploy models, we engineer the fundamental decision engine that powers our platform. At a scale of 770 million users and 100,000+ predictions per second, we are solving a multi-objective optimization problem that balances user incentives, advertiser ROI, and long-term platform health in real time. Our architecture is built on a 1PB+ behavioral data lake, providing the high-fidelity input necessary to train deep learning models that predict individual user engagement with precision. We aren’t just optimizing clicks, we are dynamically calculating optimal reward structures to sustain a global value exchange. Engineered for performance, our stack leverages Tensorflow and PyTorch for model training, NVIDIA Triton to achieve sub-100ms inference. We own the full ML lifecycle from high-level research and feature engineering to deployment and A/B experimentation. Here, you will find the autonomy, the data depth, and the massive scale required to solve the most complex optimization challenges in the adtech ecosystem. Your Mission & Who We Are Looking For: — Proven track record in production ML with 5+ years in Data Science and deployed models that moved real business metrics. — Building models that predict LTV, conversion, and user behavior across 770 million users, impacting advertiser ROAS and publisher revenue. — Deep learning as primary tool with strong hands-on experience in PyTorch or TensorFlow and production deployment handling 1M+ daily predictions. — Full ML lifecycle ownership from data extraction using Trino, Spark, or AWS Athena, through model development, A/B validation, to production deployment. — Experience with Airflow or model hosting is a strong plus. — Fluent in Python and data at scale, comfortable with pandas/polars, numpy, scikit-learn, LightGBM, CatBoost, XGBoost, and advanced SQL skills. — Ability to translate product requirements into ML logic and explain model behavior and impact to non-technical stakeholders. — AdTech experience is a strong plus. What’s in It for You? — Relocation support to Hamburg, Germany for worldwide talent. — Opportunity to build infrastructure behind impactful adtech platform reaching hundreds of millions of users and powering billions of decisions daily. — Ownership of projects impacting 770M users and pushing adtech boundaries. — Fast-paced environment with multiple daily solution deployments and learning from results. — Collaborative culture with diverse global team from over 40 countries. — Enjoy daily victories and energetic work environment.

Задачи: - Развитие алгоритмов ранжирования и рекомендаций для контента вертикали Посутка - Построение и развитие моделей улучшения качества контента - Исследование возможностей внедрения ML и AI в команде - Вместе с продуктовым руководителем вертикали формировать планы и отвечать за результат работы ML моделей Требования к кандидату: - Опыт работы от 3х лет - Опыт доведения ML-моделей до продакшена и оценки влияния на бизнес от внедрения - Опыт построения ML-моделей для персонального ранжирования, рекомендаций и предсказания CTR - Опыт использования технологий для работы с большими данными и распределенными системами (Hadoop, Spark, S3) - Знание ML-алгоритмов и применение их на практике - Владение Python и основными ML-фреймворками - Понимание различных моделей монетизации и механизмов управления ликвидности - Прикладной опыт и подтвержденные успехи в решении задач ранжирования и построения рекомендаций