Данные и ML вакансии - в офисе

Wincent is looking for a Quantitative Research Intern to analyze market signals, develop trading strategies, construct quantitative models, and participate in trading exercises.

RELOCATION TO CYPRUS Требования: • От 2 лет опыта в роли Head of Data / Data Group Lead • Уверенное владение SQL • Глубокое понимание статистики и A/B-тестирования • Опыт работы с BI-инструментами, желательно Tableau Важно: ⚠️ Готовность к релокации на Кипр и работе из офиса в Лимассоле.

Wincent is looking for a Data Platform Engineer to improve data accessibility, ensure data quality, and enhance cloud-based data processing.

Проектирование, разработка и сопровождение ETL/ELT-процессов с использованием Python и Apache Airflow. Практический опыт эксплуатации Apache Airflow, Spark (PySpark),ClickHouse и PostgreSQL в промышленной среде.

· Анализировать данные, выбирать подходящие архитектуры (от классического ML до нейросетей) и обучать модели. · Искать и тестировать новые подходы и решения, адаптируя их под наши нужды.

Разрабатывать, обучать и внедрять ML/DL-модели для бизнес-задач. Строить AI-агентов и multi-agent workflows с tool/function calling, MCP и LangGraph/LangChain (или аналогами).

• Develop and adapt computer vision/video analytics models for detection, tracking, and recognition. • Work with video pipelines, including RTSP/HTTP, decoding, sampling, and batching.

Задачи: — Обеспечивать экспертно-аналитическое сопровождение и мониторинг внедрения и развития ИИ в отраслях экономики и госсекторе.

• Own and implement AI features in production, leveraging LLMs and your own models. • Train specialized models for tasks like content moderation and recommendation.

3+ years of professional AI/ML Engineering experience. Strong Python, SQL, NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow.

Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate. This is not a traditional annotation role. You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines. We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders. Project Details Job Title: Data Labeling Associate Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Pay Rate: $34/hour   Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston, Austin. Please only apply if you meet this location requirement. What You’ll Do Evaluate AI model outputs and provide structured, high-quality feedback Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios Contribute to improving evaluation frameworks, not just executing them Identify trends in model performance and communicate insights clearly Participate in team discussions, calibrations, and stakeholder syncs Partner with leads and cross-functional teams to refine quality standards Document findings in a clear, concise, and actionable way What We’re Looking For Native-level Tamil language proficiency and a university degree (Bachelor’s or higher). B2 or superior level of English. 1–2 years of professional writing experience with strong, structured writing skills Ability to apply complex writing rules and guidelines consistently Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience Strong critical thinking and attention to detail Ability to make sound judgment calls in ambiguous situations Naturally curious about AI, technology, and how systems behave Comfortable speaking up, asking questions, and contributing ideas Strong written and verbal communication skills Ability to stay consistent while working with evolving guidelines Experience in data quality, QA, annotation, or analysis is helpful — but not required Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes. Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix. Unique Campus Features: Some locations include roof-top nature parks Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks. Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast-moving, collaborative, and increasingly central to how modern AI systems are built and deployed.

Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate. This is not a traditional annotation role. You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines. We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders. Project Details Job Title: Data Labeling Associate Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Pay Rate: $34/hour   Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston, Austin. Please only apply if you meet this location requirement. What You’ll Do Evaluate AI model outputs and provide structured, high-quality feedback Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios Contribute to improving evaluation frameworks, not just executing them Identify trends in model performance and communicate insights clearly Participate in team discussions, calibrations, and stakeholder syncs Partner with leads and cross-functional teams to refine quality standards Document findings in a clear, concise, and actionable way What We’re Looking For Native-level Marathi language proficiency and a university degree (Bachelor’s or higher). B2 or superior level of English. 1–2 years of professional writing experience with strong, structured writing skills Ability to apply complex writing rules and guidelines consistently Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience Strong critical thinking and attention to detail Ability to make sound judgment calls in ambiguous situations Naturally curious about AI, technology, and how systems behave Comfortable speaking up, asking questions, and contributing ideas Strong written and verbal communication skills Ability to stay consistent while working with evolving guidelines Experience in data quality, QA, annotation, or analysis is helpful — but not required Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes. Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix. Unique Campus Features: Some locations include roof-top nature parks Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks. Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast-moving, collaborative, and increasingly central to how modern AI systems are built and deployed.

• Analyze data and select architectures for model training. • Test new approaches and deploy models to production.

• Development of machine learning and deep learning models. • Building solutions in NLP and Deep Learning.

построение моделей для управления розничным кредитным риском (PD, Expected Loss, прогнозирование событий жизненного цикла клиента). поддержание и изменение процессов калибровки и дообучения моделей.

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, 30M+ 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 Generative video has a problem most subscription businesses don't: every unit we sell costs us real compute. A stolen password costs a streaming service nothing. An account farm on an unlimited generation plan costs us GPU-seconds, at scale, every hour it runs. We have found organised operators running hundreds of accounts on a single card, reselling unlimited generations, and harvesting per-account promotional grants at industrial scale. We have also found that most of our negative-margin accounts are not abuse at all — they are ordinary heavy users on an unlimited plan, which is a pricing problem wearing a fraud costume. Telling those two populations apart, at account level, before the money is gone, is this job. You own the question of who is taking value they did not pay for, and the system that stops them. You make sure: - We catch abuse before the compute is burned, not in a post-mortem that reconciles the loss - Every enforcement action has a measured precision, a measured cost of being wrong, and a way back for the customer we got wrong - The difference between fraud and unprofitable pricing is a number we can defend, not a vibe - When operators adapt — and they will, within days — we find out from a monitor, not from the monthly margin review WHAT YOU WILL DO Detection & scoring - Own the account-abuse scoring system end to end: signals, weights, thresholds, the scoring job, the monitoring, and the recalibration when it decays. - Build detection that fires early. A rule that catches 88% of the money on day 11 is worth far less than one that catches 60% at checkout, and you should be able to say why in dollars. - Work the two halves that actually matter together: linkage (who is connected to whom) and economics (what are they consuming). Either one alone produces a system nobody can switch on — linkage without economics flags families sharing a card, economics without linkage flags our best customers. - Treat cost as a gate, not a label. On an unlimited plan, being gross-margin negative is normal. Entity resolution & ring detection - Find rings, not just accounts: shared payment instruments, IP and ASN concentration, email-stem families, synchronised registration bursts, behavioural fingerprints, automation signatures. - Do it without a reliable device fingerprint — we do not have one today, and part of this job is telling us what it would be worth and what it would cost. - Know where graph methods break. Transitive closure through a shared card will happily merge thousands of unrelated people into one "ring"; we have done exactly that and thrown the result away. Enforcement, policy & the cost of being wrong - Design graduated, reversible actions — throttle, rate-limit, step-up verification, hold, pre-grant refusal, manual review queue, ban — and match the severity to the confidence. - Own the false-positive budget explicitly: how many paying customers are we willing to inconvenience to save a dollar of compute, and what is the appeal path for the ones we get wrong. - Build the pre-grant gates. The cheapest abuse to stop is the kind we decline to enable in the first place — a trial we do not grant costs nothing to claw back. - Partner with Payments on Stripe Radar, chargebacks and dispute economics; with Support on the queue your model creates; with Legal on what our Terms actually entitle us to do. Measurement & adversarial monitoring - Quantify what the system is worth in money — COGS avoided, net of the revenue you destroyed and the support cost you created. Keep that number current. - Run holdouts even on enforcement, wherever it is legal and safe to do so, because otherwise you will never know what your rules are worth. - Monitor for adaptation, not just drift. Model decay here is caused by an adversary, and it looks different: a signal stops firing, a ring changes shape, a cohort's behaviour shifts inside a week. - Watch your own inputs. A detection system that silently loses a feature degrades to a system that cannot reach its own threshold — and reports nothing wrong while doing it. Making it usable - Ship scores and reasons, never scores alone. An analyst, a support agent or an auditor must be able to read why an account was actioned. - Produce evidence that survives outside the company. Our abuse work has already been used in an external audit and in Terms-of-Use enforcement; write for that standard. - Work in SQL, Python and BigQuery on raw event, payment and generation data. We use AI heavily — resourcefulness beats syntax. WHO WE'RE LOOKING FOR - Experience building abuse, fraud or trust-and-safety detection that actually took action on real users — payments fraud, promo abuse, multi-accounting, bonus abuse, account takeover, marketplace or gaming abuse. Anti-money-laundering and credit-risk backgrounds transfer if the modelling was yours. - Genuine understanding of the adversarial setting: that your counterparty responds, that your training labels are generated by your own past decisions, and that offline AUC on a censored label set is close to meaningless. - Strong SQL and Python on raw event data without help: joins across payments, usage and identity, window functions, cohort replay. Gradient boosting, and the judgment to know when a two-condition rule beats a model. - Comfort with graph and linkage thinking — connected components, shared-attribute clustering, and an instinct for when a link is evidence and when it is coincidence. - Precision-first evaluation instincts: precision at the operating point, cost-weighted error, and why you would take 20% recall at 90% precision over the reverse in an enforcement system. - Unit-economics literacy: gross margin per account, what compute actually costs us, why a negative-margin customer may be entirely legitimate. - The judgment to escalate rather than act when the evidence is thin, and to say so in writing. - Clear written and spoken English, B2+. Backgrounds that often do well: - Fraud / risk data scientists from fintech, payments, marketplaces, gaming or crypto - Trust & safety and platform-integrity teams who owned detection and enforcement policy - Growth or product data scientists who ended up owning promo-abuse because nobody else would - Analysts from payment processors, PSPs or chargeback/dispute operations who moved into modelling WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to build content-moderation or NSFW classification — that is a different function here - Would ship a ban list without knowing its precision - Optimize AUC on a label set your own enforcement created, and call it validated - Need a labelled fraud dataset to exist before you can start - Treat every negative-margin account as an abuser - Are uncomfortable that being right 90% of the time means being wrong about real, paying, legitimate people — and that you own what happens to them - 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) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

-Проектирование и сопровождение БД: разработка и поддержка объектов базы данных (таблицы, представления, индексы, хранимые процедуры) в среде Greenplum. -Разработка витрин данных: проектирование и расчет показателей для витрин данных под бизнес-задачи с использованием SQL.

Задачи: — Отвечать за всю AI-часть продукта: архитектура AI-системы от выбора LLM-моделей до построения RAG-pipeline и интеграции компьютерного зрения, принимать все ключевые технические решения — Обеспечивать качество рекомендаций, повышая конверсию на каждой итерации — Проводить быстрые итерации с выкатыванием изменений каждую неделю, сбором метрик, анализом и улучшением — Проводить эксперименты: A/B тесты промптов, гибридный поиск (текст + визуал + отзывы), мультимодальные модели, быстро проверять гипотезы на реальных пользователях — Масштабировать систему с 2000 туров на Мальдивы до 50,000+ туров по трём направлениям с тысячами запросов в день — Строить продукт с нуля, быстро экспериментировать и влиять на архитектуру Требования: — Опыт с продакшен LLM-приложениями, построение RAG-систем или чат-ботов на основе GPT/Claude — Экспертный уровень промпт-инжиниринга: правильная структура промптов, few-shot examples, chain-of-thought — Опыт работы с эмбеддингами и векторным поиском, понимание similarity search, hybrid search, reranking — Python на уровне продакшена: чистый, быстрый, поддерживаемый код Будет плюсом: — Опыт с компьютерным зрением и мультимодальными моделями (CLIP, GPT-4o, Claude 4.5 Sonnet) — Опыт с high-load системами и оптимизацией latency — Понимание рекомендательных систем — Понимание MLOps: мониторинг моделей, версионирование, A/B тестирование — Опыт построения recommendation систем и MCP для LLM систем — Личный опыт использования LLM для автоматизации задач Софт-скиллы: — Продуктовое мышление: AI как инструмент решения бизнес-задач — Умение работать с метриками: конверсия, время сессии, escalation rate — Быстрота принятия решений: выкатывать несовершенное решение и тестировать — Умение объяснять сложное простым языком для команды и бизнеса Условия: — Официальное оформление по ТК РФ — График 5/2, офис, сокращённый рабочий день по пятницам до 15:30 — Корпоративные программы обучения и развития — Медицинское обслуживание: ДМС и собственная медсанчасть — Специальные предложения от компаний-партнеров (рестораны, магазины, отдых, мероприятия) — Организация отдыха сотрудников и их детей, оплата детского сада — Аккредитованная IT компания — Офис на станции м. Курская — Корпоративные условия на приобретение туров — Дополнительный оплачиваемый отпуск 3 дня, оплата больничного — ДМС для членов семьи по корпоративным условиям — Корпоративные условия на изучение английского и занятия спортом