Вакансии

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Manager, Product Applied Science to build and lead a team at the core of Garner's product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is a hands-on management role. You are responsible for the overall technical direction of your squad, building alongside your team of Applied Scientists to solve some of the hardest problems at the company. This is not a dashboards or descriptive-analytics team. Your team owns production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is leading a research pod at a top hedge fund. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Lead a team of 4-6 — hire, coach, and develop them, hold the bar on both rigor and speed, and help raise that bar across Applied Science Own the Population Health team and their outputs as a core responsibility Own your team's roadmap and delivery: what gets built, in what order, and whether it ships Set technical direction, and get in the weeds to do it — stay close enough to the code and the data to call the approach yourself, whether that's machine learning, optimization, a heuristic, or a simple rule, and set the standard for how your team makes that call Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Put your own hands on the problems where your judgment matters most — the highest-stakes, least-defined work — and deliver the algorithmic step-changes that move metrics like total-cost-of-care savings, steerage, and member engagement Turn ambiguous business goals into a clear problem set alongside Research, Product, Engineering, and business stakeholders, and represent your team's work to senior leadership Set the quality bar: review your team's work with rigor, and define the metrics that decide whether a solution is working Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred Deep technical credibility, with the range and judgment to lead scientists who are experts in their own right, and the willingness to stay hands-on Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them A bias toward action, quickly translating ideas into working prototypes to test approaches Strong communication skills, including at the executive level, with a track record of driving alignment across teams and functions A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to build and lead the team behind Garner's algorithmic systems — shaping how members find better care, and how applied science gets done at Garner, while staying close to the work yourself. What you'll get here: You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Compensation Transparency: The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Staff Applied Scientist to join our Applied Science team. Garner is hiring Applied Scientists to design and ship the algorithmic systems at the core of our product. Our members rely on us to answer hard questions — Which doctor should I see? What will it cost? When should we reach out, and how? — and the quality of those answers is determined by the algorithms behind them. This is not a dashboards or descriptive-analytics role. You will own production systems end-to-end: framing the problem, defining the objective function, choosing the right approach (ML, optimization, heuristics, expert systems, or a hybrid), shipping it, and improving it against real-world outcomes. The closest analog outside healthcare is a quantitative researcher at a top hedge fund. This is a player-coach role. You will be a hands-on-keys Applied Scientist, while also leading a small team that helps you deliver on your roadmap. Your time will be split between your own technical work and working with your team to shape how they approach their problems. This role is a good fit for someone who wants to develop their management toolkit while continuing to work closely on their own technical work, and it can lead either towards management or a deeper senior IC track. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the most ambiguous, high-stakes problems facing the company end-to-end, and set how the team frames and approaches them Frame messy, real-world healthcare and business constraints into clear objectives, tradeoffs, and decision frameworks Define the set of metrics needed to judge whether a solution is working, and validate solutions before they ship Choose the right approach for each problem, from machine learning to optimization to heuristics to simple rules, based on what the problem actually calls for, and set the standard for how the team selects and applies these approaches Deliver algorithmic breakthroughs that move the company's most important metrics, pioneering approaches that become how applied science is done at Garner Lead a small team — set their technical direction, unblock them when they're stuck, and share accountability for their growth and the quality of what they ship Review Applied Science work at the highest level across the company, ensuring the methods used across teams are sound and correctly applied Build a deep understanding of the healthcare economy and Garner's place in it To make the role concrete, here are three problems on our near-term roadmap: Provider tiering optimization. Build a tiering algorithm that jointly optimizes geographic access and total-cost-of-care savings across our doctor network. The objective function, constraints, and tradeoff surface are all open design questions. AI primary care doctor. Fine-tune and productionize an LLM-based primary care experience on our website, including the evaluation harness, guardrails, and ongoing quality monitoring needed to ship a medical-adjacent product safely. Member engagement model. Build an ML system that ingests claims data and in-app behavior to choose the right channel and moment for each touchpoint — SMS, push, phone, or email — to influence member behavior toward better-quality, lower-cost care. The ideal candidate has: 6+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 4+ years of industry experience with a relevant advanced degree, PhDs preferred A bias toward action, quickly translating ideas into working prototypes to test approaches Strong applied problem-solving skills, with the ability to define good metrics and then deliver solutions that improve them Recognized technical authority, with the judgment to ensure the techniques used across an organization are sound Strong interest in mentoring or technically leading other Scientists — formal management experience is welcome, but not required Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem Strong communication skills, including at the executive level, with a track record of driving alignment across an organization A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you’ll get here You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, SQL, AWS, Snowflake, pandas, XGBoost, PyTorch, HuggingFace, modern LLM tooling and eval frameworks. We pick tools based on the problem, not the resume — bring your judgment. This is a unique opportunity to work on high-impact problems in healthcare — shaping how members find better care through algorithmic systems that directly influence healthcare outcomes, and helping the scientists around you do the same. Compensation Transparency: The target base comp range for this position is $300,000-$390,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Data Analyst (LTV / Predictive Analytics) StellarTech is a B2C mobile publisher in EdTech: subscription apps, in-app monetization and performance marketing at scale. LTV prediction is how we decide where to invest, and the forecasting pipeline is already live. We are opening this role to have one senior analyst own its accuracy and its evolution. This is research and predictive analytics, not reporting: everything that can be standardised is already automated by Data Engineering, so the work is hypotheses, factor analysis and improvements to the prediction algorithm itself. You report to the Head of the Operational Department and work directly with Product, UA, Finance and Data Engineering. What you will do Own and develop the LTV prediction algorithm: find the parameters and factors that increase forecast accuracy, prototype improvements in SQL, measure the gain, then hand the logic over to Data Engineering. Validate forecasts on closed cohorts, monitor forecast quality over time and explain where exactly a model deviates from actual LTV. Research what moves LTV: product features, segments, purchase sources (for example, purchases coming through CRM vs other sources), and adapt the prediction logic to them. Run analytical research end to end, from a vague question to a documented recommendation for Product, UA and Finance: where payback improves, where to concentrate media buying, what changes unit economics at company level. Automate analytical routines and model quality monitoring together with Data Engineering. Requirements Critical 4+ years in Data Analyst / Senior Data Analyst / Product Analyst roles with a product analytics focus: measuring the impact of factors on business metrics, not marketing attribution. Strong SQL: complex queries, window functions, optimisation. Here SQL is the main instrument for prototyping and testing the algorithm. Solid mathematical statistics and understanding of how ML models work: you can build and validate a forecast, judge model quality (MAE, RMSE, MAPE, R2), run factor analysis and explain where a model broke. Product background with in-app monetization and its economics: LTV, cohorts, retention, ROI, CAC, ARPU, payback. Subscriptions are ideal; other in-app B2C models also work. Ad-only monetization is a different pattern and is not a fit. A proven end-to-end pattern: you take a vague question, form a hypothesis, dig into it and bring it to a recommendation. There is no micromanagement here and almost no standard tasks. Ability to back conclusions with data and to communicate them to both technical and business stakeholders. Non-critical Python (pandas, numpy, scikit-learn) for analysis and modelling: welcome, but not a blocker if the statistical and analytical thinking is there. A/B testing and statistical hypothesis testing. BI tools (Tableau, Looker, Power BI). Cloud DWH and large data volumes (Athena, BigQuery, Snowflake, Redshift or similar). Will be a plus Feature engineering and model interpretation (SHAP, feature importance). Mobile products, gaming or subscription businesses. Our stack: Amazon Athena, S3-based DWH, CatBoost, Tableau, Git, Jupyter. Why You'll Love Working With Us Impactful Work: Your contributions will directly shape the future of our company. Innovative Environment: We're all about trying new things and pushing the envelope in EdTech. AI solutions Wealth: We offer a competitive salary. Balance: Flexible paid time off, you get 21 days of annual leave + 10 bank holidays. Collaborative Culture: Work alongside passionate professionals who are as driven as you are. Hiring process Intro call with the recruiter. Technical interview: SQL, statistics and ML fundamentals, plus an analytical case on how you build a plan to investigate a problem. Final interview with the department head. Optionally, a 30-minute culture fit conversation with HRBP. If this is the kind of work you want to own, send us your CV. We will come back to you with the next step.

Требования: — HBO или университетский уровень мышления и работы — Опыт разработки на Python не менее 3 лет — Глубокие знания Python и библиотек, таких как Pandas — Опыт работы с Django, Flask или аналогичными фреймворками — Опыт работы с SQL и NoSQL базами данных — Опыт написания unit, integration и end-to-end тестов — Опыт работы с инструментами CI/CD YoungCapital NEXT — нидерландская кадровая компания и агентство по подбору персонала, специализирующееся на трудоустройстве и развитии молодых специалистов в IT, финансах и государственном секторе.

Location: Vietnam The Company Our client is a financial technology (FinTech) company specifically focused on bridging the gap between man and machine when it comes to investing. They are building an AI-driven trading and investing platform covering stocks, futures, forex, and crypto. They develop quantitative strategies, portfolio models, copy trading solutions, and low-latency execution systems. Our client aims to be the world's most trusted AI trading platform where sophisticated market intelligence meets effortless execution, enabling every trader to compete with institutional-level strategies while maintaining full control over their investment decisions. The Role Our client is seeking a Senior Quantitative Developer to design, build, test, and deploy systematic trading strategies. The ideal candidate combines strong software engineering skills with quantitative finance and algorithmic trading experience. Key Responsibilities Develop and maintain quantitative trading strategies. Build backtesting, optimization, and portfolio construction frameworks. Implement walk-forward analysis, Monte Carlo testing, and robustness validation. Work with market data including equities, futures, forex, and crypto. Integrate machine learning models into trading workflows. Collaborate with Java execution and platform engineering teams. Deploy research into production trading environments. Monitor strategy performance and improve risk-adjusted returns. Success Metrics Build 100+ validated strategy candidates annually. Develop production-grade research infrastructure. Improve portfolio Sharpe ratio and reduce drawdowns. Create scalable AI-driven trading models. Requirements 5+ years Python development experience. Strong knowledge of Pandas, Polars, NumPy. Experience with VectorBT, Backtrader, or QuantConnect LEAN. Strong statistics and quantitative finance knowledge. Portfolio optimization and risk management experience. Experience with futures, forex, equities, or crypto trading. Knowledge of PostgreSQL, TimescaleDB, and cloud environments. Git, Docker, CI/CD experience. Preferred Skills Machine learning (XGBoost, LightGBM, PyTorch). FIX protocol knowledge. Interactive Brokers, Alpaca, or LMAX integrations. Java or C++ exposure. Experience with institutional trading systems. Benefits Competitive salary. Performance bonus. Stock options/equity consideration. Opportunity to work on a global AI trading platform.

Задачи: - Разработка микросервисов управления данными Модели Космической Системы; - Разработка пайплайнов обработки данных ETL/ELT; - Интеграция вычислительных модулей (микросервисов); - Реализация процессов контроля качества данных; - Разработка моделей данных и согласование дата-контрактов; - Работа с СУБД Postgres, Clickhouse; - Обработка и анализ данных в Pandas, NumPy, Polars. Стек разработки: Python 3.12+, FastApi (Pydantic v2), Numba, Numpy, Pandas, SQLAlchemy 2+, Ruff+MyPy (lint), Kafka (FastStream), Redis (Dragonfly), Matlab, Postgres, Clickhouse. Требования: - Инженерное или техническое образование (обязательно); - Опыт разработки на Python от 3х лет, на позиции Middle - от 1 года; - Опыт работы с библиотеками для анализа данных: Pandas, NumPy, Polars; - Понимание принципов проектирования и работы с хранилищами данных; - Опыт работы с базами данных PostgreSQL, ClickHouse; - Опыт работы с инструментами ETL/ELT (например, Airflow, Dagster, PandasETL); - Понимание принципов работы систем потоковой обработки данных; - Опыт в разработке веб-приложений (FastAPI); - Опыт работы с контейнерами Docker; - Опыт работы с инструментами командной разработки: Confluence, Jira, Git. Условия: - Работа в аккредитованной ИТ-компании; - Возможность выбора формата работы (офис/гибрид); - Комфортный современный офис (м. Мнёвники); - Оформление в соответствии с ТК РФ; - Конкурентный уровень заработной платы - на уровне лидеров ИТ/телеком-индустрии.

Обязанности: • Обработка и анализ данных в Excel (основной инструмент работы). • Очистка, нормализация и структурирование данных из различных источников. • Объединение данных из нескольких файлов в единую структуру по заданной логике. • Создание сводных таблиц и отчетов. • Автоматизация задач с использованием формул и макросов (VBA). • Загрузка и обновление данных в базе данных. • Работа с SQL-запросами (выборка, объединение, фильтрация данных). • Подготовка отчетов и дашбордов (Power BI или аналоги). • Самостоятельный разбор задач и поиск оптимальных решений. Требования: • Уверенное владение Excel (формулы, сводные таблицы, Power Query, работа с большими объемами данных). • Базовые знания SQL (SELECT, JOIN, фильтрация данных). • Навыки автоматизации (VBA или альтернативные инструменты). • Опыт работы с BI-инструментами (Power BI или аналоги). • Базовые знания Python (Pandas, обработка данных). Условия: • Работа в международной строительной компании на промышленном проекте федерального значения (Ленинградская область, п. Усть-Луга); • Трудоустройство по ТК РФ с первого рабочего дня и на весь период выполнения проекта; • "Белая" заработная плата без задержек; • Все переработки оплачиваются; • Срочный трудовой договор на период реализации проекта по 2028 г.; • Для иногородних сотрудников комфортное проживание в общежитиях гостиничного типа на территории строительного объекта, спальные и санитарные принадлежности предоставляются; • Столовая в шаговой доступности, 3-разовое питание; • Медицинская помощь; • Для местных сотрудников ежедневный трансфер в Кингисепп, Сосновый бор, п. Усть-Луга и обратно; • Трансфер на выходные в г. Кингисепп; • Ежегодный оплачиваемый отпуск; • СИЗ, спецодежда, по сезону; • Детальные условия и размер заработной платы обсуждаются по результатам собеседования.

Задачи: — Разработка ИИ-решений для образовательных систем — Разработка автоматизированных пайплайнов для проверки и оценки ответов — Проведение экспериментов с большими языковыми моделями (LLM), промптами и методами дообучения — Подготовка, обработка и анализ данных Требования: — Опыт работы в Data Science, Machine Learning или NLP от 2 лет — Хорошее знание основ машинного и глубокого обучения — Уверенное владение Python — Продвинутый уровень SQL — Опыт работы с библиотеками для анализа данных и машинного обучения: NumPy, pandas, scikit-learn Условия: — Работа в офисе в Астане

Задачи: - Реализация высоконагруженных конвейеров обработки данных для обеспечения надежной и бесперебойной репликации данных из ИТ-систем Банка. - Реализация комплексных задач по подготовке данных в целевых аналитических хранилищах (DataLake, SandBox, FeatureStore) для построения признаков, необходимых для разработки моделей машинного обучения. - Разработка и поддержание в актуальном состоянии документации по разработанному функционалу. - Своевременное отражение статуса выполнения задач в Jira. - Проверка качества кода (код-ревью), написанного инженерами и младшими инженерами данных. Требования: - Python - уверенное знание структур данных и алгоритмов, эффективное применение принципов ООП и ФП (Функциональное программирование), опыт написания модульных и интеграционных тестов, знание и опыт применения библиотек обработки и анализа данных - numpy, pandas. - Опыт разработки и внедрения в промышленную эксплуатацию сервисов загрузки и обработки неструктурированных и слабо структурированных данных (текст, xml, json) из внешних источников. - Способность разобраться с API поставщиков данных, используя доступную документацию. - SQL - умение создавать сложные запросы с использованием аналитических оконных функций и использовать инструменты профилирования для оптимизации их производительности, опыт работы с БД Oracle, Postgres, Greenplum. - Уверенное знание и опыт работы с инструментами разработки, планирования и мониторинга рабочих процессов (workflow engines) пакетной обработки данных. - Airflow. - Опыт разработки сложных, высоконагруженных приложений обработки данных на основе PySpark, уверенное знание настроек Spark и их влияния на производительность приложений Spark.

Задачи: — Анализировать данные по мошенничеству, AML и поведению пользователей для выявления пробелов в логике обнаружения и возможностей оптимизации правил. — Изучать паттерны поведения пользователей для различения легитимной активности от мошеннической или подозрительной. — Разрабатывать, внедрять и совершенствовать правила обнаружения в областях мошенничества, AML и санкций. — Исследовать новые типологии и неизвестные атаки, предлагать стратегии их смягчения. — Постоянно улучшать показатели мошенничества и ложных срабатываний, балансируя точность обнаружения и удобство для клиентов. — Проводить контролируемые эксперименты и A/B тесты для оценки влияния на уровень мошенничества и качество оповещений. — Выполнять контроль качества оповещений, анализировать ложные срабатывания и повышать точность. — Сотрудничать с командами продукта, инженерии и дата-сайенс для улучшения моделей риска, логики оповещений и автоматизации. — Использовать AI и инструменты на базе LLM для ускорения расследований и масштабирования анализа. — Готовить понятные визуализированные отчеты по трендам рисков, эффективности правил и текущим угрозам. Требования: — Опыт работы на позиции продуктового аналитика от 4 лет. — Экспертный уровень SQL, умение писать и оптимизировать сложные запросы. — Продвинутые навыки работы с инструментами визуализации данных. — Практический опыт использования AI-инструментов в аналитике и расследованиях. — Способность видеть бизнес-смысл в данных и ясно его коммуницировать. — Английский язык на уровне не ниже Upper-Intermediate. — Желательно: знания теории вероятностей и математической статистики (A/B тесты, причинно-следственный анализ). — Желательно: опыт использования Python для аналитики данных (NumPy, Pandas). — Желательно: опыт в области риска, комплаенса или борьбы с мошенничеством. Условия: — Гибридный формат работы в офисах в Лимассоле или Алматы, либо полностью удаленно вне офисов. — Поддержка релокации на Кипр (виза, пакет). — Бюджет на обучение и развитие. — Полностью оплачиваемый отпуск и больничные. — Компенсация на спорт. — Реальные перспективы роста, значительная ответственность и возможность влиять с первого дня. — Возможность развивать экспертизу и формировать будущее финтеха. О компании: Vivid — финансовая платформа, основанная в 2019 году в Берлине, быстро выросшая и работающая по всей Европе. Компания строит AI-native организацию, где автоматизация берет на себя рутинные задачи, позволяя командам сосредоточиться на стратегии и масштабируемых решениях. Миссия — помочь клиентам управлять и приумножать свои финансы с помощью современных инструментов и технологий.

Задачи: — Анализировать данные по мошенничеству, AML и поведению пользователей для выявления пробелов в логике обнаружения и возможностей оптимизации правил. — Изучать паттерны поведения пользователей для различения легитимной активности и мошеннических или подозрительных действий. — Разрабатывать, внедрять и совершенствовать правила обнаружения в областях мошенничества, AML и санкций. — Исследовать новые типологии и неизвестные атаки, предлагать стратегии их предотвращения. — Постоянно улучшать показатели мошенничества и ложных срабатываний, балансируя точность обнаружения и удобство для клиентов. — Проводить контролируемые эксперименты и A/B тесты для оценки влияния на уровень мошенничества и качество оповещений. — Выполнять контроль качества оповещений, анализировать ложные срабатывания и повышать точность. — Сотрудничать с командами продукта, инженерии и дата-сайенс для улучшения моделей риска, логики оповещений и автоматизации. — Использовать AI и инструменты на базе LLM для ускорения расследований и масштабирования анализа. — Готовить понятные визуализированные отчёты по трендам рисков, эффективности правил и текущим угрозам. Требования: — Опыт работы на позиции продуктового аналитика от 4 лет. — Экспертный уровень SQL с умением писать и оптимизировать сложные запросы. — Продвинутые знания инструментов визуализации данных. — Практический опыт работы с AI-инструментами в аналитике и расследованиях. — Способность видеть бизнес-смысл в данных и ясно его коммуницировать. — Английский язык на уровне не ниже Upper-Intermediate. — Будет плюсом: знание теории вероятностей и математической статистики (A/B тесты, причинно-следственный анализ), опыт использования Python для аналитики (NumPy, Pandas), опыт в области риска, комплаенса или антифрода. Условия: — Гибридный формат работы в офисах в Лимассоле или Алматы, либо полностью удалённо. — Поддержка релокации на Кипр (виза, пакет). — Бюджет на обучение и развитие. — Полностью оплачиваемый отпуск и больничные. — Компенсация на спорт. — Реальные перспективы роста, значительная ответственность и возможность влиять с первого дня. О компании: Vivid — финтех-компания, основанная в 2019 году в Берлине, быстро выросшая и работающая по всей Европе. Компания строит AI-native организацию, где автоматизация берёт на себя рутинные задачи, позволяя командам сосредоточиться на стратегии и масштабируемых решениях. Миссия — помочь клиентам управлять финансами и достигать успеха.

Задачи: — разрабатывать и поддерживать микросервисы на FastAPI и взаимодействовать со смежными командами для интеграции компонентов и ML-моделей в сервисы — проводить системный анализ бизнес-требований при проектировании архитектуры и API (REST, Kafka) для новых сервисов (логика сценария, состояния, переходы, ограничения, ошибки, fallback-ветки, затронутые компоненты и критерии приемки) — развивать AI pipeline агента: корректировать и продумывать промпты для GigaChat, реализовывать RAG классификации, настраивать пороговые значения, настраивать эмбеддинг модели, реализовывать ограничения (лимиты) вызовов LLM с целью оптимизации затрат у ускорения работы — оптимизировать производительность сервисов на всех уровнях: код (asyncio, threading), хранение данных и запросы (PostgreSQL, продвинутый SQL, индексы, шардирование), кэширование (Ignite), работа с очередями (Kafka) — участвовать в проектировании тестовых сценариев и создании тестовых датасетов. Контролировать качество реализации, проводить глубокий анализ дефектов, определять их корневые причины. Требования: — опыт разработки на Python от 3 лет, включая самостоятельное проектирование архитектуры компонентов сервисов на основе бизнес-требований — практический опыт создания высоконагруженных и отказоустойчивых микросервисов на FastAPI, а также продуктов с внедрением ML решений — понимание принципа работы LLM/AI агентов, prompt-engineering, RAG, embeddings, классификации, latency и ограничений генеративных моделей — практический опыт работы с фреймворками ML/DS:(LangChain/GigaChain, Scikit-Learn, Pandas и др.) — глубокие знания в области асинхронных приложений, базовый SQL, опыт работы с брокерами сообщений (Kafka), Docker, Kubernetes — высокая культура совместной работы используя BitBucket/Jira, бережное отношение к коммитам, знание процессов Agile (Scrum) — опыт анализа логов через написание парсеров и диагностических инструментов. Условия: — комфортный современный офис на м. Кутузовская — формат работы: офис на время адаптации и далее гибрид (2–3 дня в неделю в офисе) — ежегодный пересмотр зарплаты, годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендации друзей в команду Сбера.

Location: Remote US At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. We’re looking for an intelligent, driven professional to join our Bank Model Risk Management (MRM) team. This team seeks to establish, maintain and oversee an effective MRM framework to identify, quantify, monitor, mitigate and report on model risk. You will have an outstanding opportunity to work cross-functionally to develop a profound understanding of models that drive critical business decisions, and add value to the Bank by mitigating risks due to ineffective model design or model misuse. What You'll Do Full-Stack Model Validation: Conduct rigorous, independent validations of sophisticated credit/fraud models—including machine learning and traditional statistical models—focusing on conceptual soundness, data integrity, and performance stability. Advanced Quantitative Monitoring: Develop automated, independent monitoring suites in Python to track KRI/KPI drift, population stability (PSI), and feature importance shifts in real-time. Remediation & Technical Advisory: Partner with 1st-line Model Developers to drive the remediation of validation findings, ensuring models and strategies are not only compliant but mathematically robust. Audit & Regulatory Liaison: Partner with Internal Audit, Internal Controls, and Compliance to facilitate the timely resolution of audit and regulatory requests. Affirm Bank: Work for the internal Bank team to support the build out of the Bank Model Risk Management function. Support the model validation requirements for Bank owned models. What We Look For 7+ years of professional experience in a highly technical capacity, such as Credit/Fraud/Financial Risk Modeling, Model Validation, or Quantitative Analytics Deep understanding of the consumer credit lifecycle and/or fraud detection. Technical familiarity with loss forecasting/fraud prediction, and stress-testing frameworks. Expert-level proficiency in Python (specifically pandas, scikit-learn, statsmodels) for replicative modeling and backtesting. Mastery of SQL for wrangling large-scale, distributed datasets and performing complex data lineage audits. A natural problem-solver with a meticulous eye for detail, a deep curiosity for how strategies perform, and sharp critical-thinking skills. Exceptional interpersonal and communication skills, with a proven ability to translate complex technical ideas for any audience. Base Pay Grade - O Equity Grade -  USA 12 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.) USA base pay range (CA, WA, NY, NJ, CT): $220,000 - $280,000 USA base pay range (all other U.S. states): $195,000 - $255,000 Please note that visa sponsorship is not available for this position. #LI-Remote Remote-first with flexibility built in Affirm is proud to be a remote-first company. Most roles can be done from almost anywhere within the country of employment. Some positions may occasionally require in-person work at an Affirm office, and a few are office-based due to the nature of the work. All new hires will be invited to attend an in-person onboarding experience. Benefits designed for you Our benefits reflect our commitment to care, transparency, and flexibility. Here are a few highlights: Health coverage at no cost: We cover 100% of premiums for employees and their dependents. Spending stipends: Monthly stipends support your tech setup, and the ability to choose health and wellness options that are right for you. Time off to recharge: Flexible time off and generous holiday calendars help you rest when you need to. Own a piece of what you build: Our employee stock purchase plan (ESPP) lets you buy Affirm stock at a discount. We’re committed to providing an inclusive interview process, including accommodations for candidates with disabilities. If you need support, we’re happy to help. For positions based in San Francisco or Los Angeles: Affirm considers qualified applicants with arrest and conviction records, as required by law. By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and consent to the use of your personal information as described.

Workplace: remote Requirements 2+ years with Python including strong knowledge of data libraries (pandas, multiprocess, Google Cloud SDK) 2+ years of experience in ETL/ELT technologies (REST API, GraphQL, DBT, etc. ) and data pipeline orchestration (e.g., Airflow, Astronomer, DBT cloud, etc) Hands-on experience with Snowflake Problem-solving and root cause analysis in live production systems along with experience querying and transforming large volumes of data, data quality and data validation Experience in SQL (SQL Server, Oracle, MySQL, BigQuery, Snowflake) Comfortable working with serialized data format (JSON, AVRO, XML, CSV) as well as compressed and encoded files Comfortable with ingesting data using REST APIs, data transformations and data loading/unloading techniques English proficiency sufficient to work in an international environment Nice to have Capable of designing and implementing data pipelines over cloud architecture Experience working with CI/CD deployments Experience working with GIT We offer Projects for such clients as PayPal, Wargaming, Xerox, Philips, adidas and Toyota Competitive compensation that depends on your qualification and skills Career development system with clear skill qualifications Flexible working hours aligned to your schedule Options to work remotely Corporate medical insurance covering services of private and public medical centers English courses online Corporate parties and events for employees and their children Gym membership compensation, corporate sport competitions (cybersport included) 5 days of paid sick leave per year with no obligation to submit a sick-leave certificate Any questions? Anna hh@itransition.com

Location: Lisbon, Portugal JoomPulse is an AI analytics tool for Mercado Livre and Shopee sellers, providing insights into the most profitable products and fastest-growing categories — helping sellers boost their profits with data-driven decisions. JoomPulse is one of the key businesses of Joom Group, an international tech-centric group of e-commerce companies that was founded in 2016 in Latvia. Joom Group also includes the following businesses: Joom, a global cross-border marketplace; JoomPro, the first and only end-to-end cross-border B2B marketplace; and Onfy, a pharmaceutical marketplace in Germany. Joom Group has offices in Latvia, China, Germany, and Brazil, with its headquarters in Lisbon, Portugal. We work as one international team, sharing knowledge and collaborating across countries, businesses and products. Currently, we are looking for an Senior Product Analyst. The JoomPulse Product Analyst owns the three things that decide whether the product grows: The quality of the data on the platform The product analytics that tell us whether new features and new data actually move conversion and retention The market analysis that turns our marketplace data into insight for large corporate data clients and for the press This person makes sure we can trust our own data, makes sure every significant feature and data release is measured against its real effect on conversion and retention, and packages our marketplace data into external-facing market analysis (category trends, pricing, market dynamics) that lands with corporate clients and journalists. Responsibilities Transform imperfect data into clear, actionable insights that drive decisions Conduct product research (identify growth opportunities, answer high-level questions, and formulate hypotheses for the team) Analyze user behavior patterns and independently uncover insights that are meaningful for the product Influence goal setting and project prioritization within the team based on data Develop self-service capabilities in the domain (structure data to make it easily usable by AI tools, and automate routine analysis so the team can independently answer basic questions) Identifying growth opportunities for the product, analysis and sales forecasting, including identifying seasonality and trends Requirements 5+ years of experience in data analysis Ability to write complex SQL queries (complex joins, window functions, CTEs, query optimization) Product-analytics depth (retention (paid & usage), cohort analysis, funnels, activation, ARPU/LTV, engagement) Knowledge of mathematical statistics and practical experience applying it Hands-on with a cloud warehouse (BigQuery or equivalent) BI and data storytelling (builds and maintains dashboards (Metabase / Looker Studio or equivalent)) Skill in selecting appropriate data evaluation methods: analytics, research, or using external data Ability to tackle complex problems in situations with incomplete data Attention to detail and ability to see tasks through to completion Preferred Built a data-quality or data-observability system from scratch and owned it Owned product analytics on a data / analytics product specifically Python (pandas / numpy) for analysis and automation and PySpark / big-data processing for the more technical work B2B SaaS experience Experience in marketplace analytics and deep understanding of marketplaces base metrics and ranking algorithms We offer Compensation package: base salary and performance-based bonuses Office-first: flexible hours with a possibility to work remotely 52 days per year, and 22 days of paid annual leave Care & Wellbeing: health insurance (including dental care) for employees and their children, daily meal allowance, and 100% paid sick leave AI-first approach: licenses and access to a wide range of AI tools Team & Growth: collaboration with colleagues across Portugal, Brazil, Latvia and China, with opportunities for promotions, professional trainings, and English courses Community & Engagement: annual team building activities, knowledge-sharing workshops, and a strong sense of team work B efore applying for the above position please review our Candidate Privacy Notice here . By responding to the vacancy, you acknowledge that you have read our Privacy notice.

Location: Athens; Limassol; Nicosia; Thessaloniki Workplace: hybrid The Role:  We are looking for a Senior Data Scientist specializing in Graph AI to join our growing Data Science team. In this role, you will design and deploy production-grade graph machine learning solutions, working with Graph Neural Networks (GNNs), graph databases, and large-scale datasets to solve complex real-world problems.   You will play a key role in shaping the company's Graph AI capabilities, collaborating with cross-functional teams and mentoring other data scientists while delivering innovative, business-impacting solutions. The main responsibilities of the position include: Design, develop, and deploy Graph Neural Networks (GNNs) and graph-based machine learning solutions Transform large, complex datasets into graph representations and extract actionable insights using advanced analytics, graph algorithms, and statistical techniques. Drive the adoption of Graph AI across the organisation by identifying new use cases and contributing to the evolution of the company's data science strategy. Communicate complex technical concepts and model outcomes clearly to both technical and business audiences. Ensure high standards in data preparation, feature engineering, model evaluation, documentation, and code quality . Leverage technologies such as Python, Graph Neural Networks, graph databases, modern ML frameworks, and cloud-based data platforms to build innovative data solutions Ensure adherence to best practices in data quality, model monitoring, version control, and reproducible analytics Lead and supervise a team of data scientists working on graph-related tasks Partner closely with Data Engineers, Software Engineers, Product Managers, and business stakeholders to translate business challenges into scalable Graph AI solutions. Main requirements: University degree in Mathematics, Physics, Computer Science, Engineering, Data Science, or a related quantitative field At least 6 years of experience in data science, machine learning and AI , including hands-on experience in designing, training and optimising Graph Neural Networks (GNNs) for production applications. Experience working with relational and non-relational databases Solid experience working with graph databases (e.g., Neo4j, Neptune) and proficiency in graph query languages (e.g., Cypher or Gremlin). Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly Strong programming skills in Python and experience with Pandas, NumPy , and similar data analysis libraries Strong analytical thinking, problem-solving ability, and attention to detail . Excellent communication skills and the ability to work collaboratively in a team environment Fluent in English Benefit from: Attractive remuneration package plus performance related reward Private health insurance Corporate pension fund Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Bring It to Life – Role-Specific Take-Home Task Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy and Data Protection Notice and the Confidentiality Undertaking.

Компания: Omega Solution. Локация: Польша. Формат работы: удалённо. Проект: Build and maintain integrations and automation processes around SQL Server and the Microsoft 365 ecosystem. The focus is on backend and automation development, not web applications. Требования: — Strong commercial experience with Python 3.12+ — Experience developing backend/batch applications (no Django/FastAPI required) — Solid knowledge of SQL Server, pyodbc и SQLAlchemy — Experience integrating with Microsoft 365: SharePoint Online, Microsoft Graph API, MSAL, Service Principals, Certificate-based authentication — Experience with pandas — Familiarity with pytest — Experience deploying applications to Windows Server/VM — Experience with Azure DevOps — Building standalone executables using PyInstaller Плюсы: — Docker — Azure Functions — Azure Key Vault — Microsoft Teams — SharePoint — GraphQL Условия: — полная занятость — удалённо (Польша) — зарплата от 3 000 $

Кто нужен: Опыт от 3-х лет в e-commerce, Python (pandas), SQL, понимание коммерческих метрик (gmv, конверсия, юнит-экономика). Будет плюсом опыт с dbt и ClickHouse. Задачи: — анализ данных — разработка витрин для визуализации — верификация данных — поддержка коммерческих решений через аналитику. Условия: — полная удаленка — гибкий график (10:00–18:00, главное — результат) — ревью 2 раза в год — оплата курсов и конференций