Jobs Data Science

About the Role
We are seeking an AI & Data Science Consultant to drive the discovery, strategy, and end-to-end delivery of advanced AI initiatives for EPAM’s global clients. This role is a unique hybrid, blending strategic business consulting with deep practical engineering. You will collaborate directly with client executives and EPAM’s delivery teams to transform complex business challenges into scalable, production-ready AI, Machine Learning, and Agentic solutions.
 

Key Responsibilities

• Consult & Strategize: Lead client-facing discovery sessions and workshops to identify high-value AI opportunities, define MVP scopes, and map out strategic delivery roadmaps.
• Design & Architect: Shape scalable AI products incorporating Generative AI, RAG, multi-agent frameworks, semantic layers, and advanced analytics.
• Bridge the Collaboration: Serve as the central link connecting business stakeholders, data scientists, ML engineers, DevOps, and product teams.
• Drive Delivery & MLOps: Apply a strong engineering mindset to ensure solutions are secure, production-ready, and smoothly integrated into cloud infrastructures.
• Support Business Growth: Partner on pre-sales activities, solution estimation, and proposal development while contributing to EPAM’s internal AI and MLOps offerings.
   

Requirements

• Professional Experience: 3+ years of hands-on experience in Data Science, ML, or Applied AI, with a proven track record of bringing models from concept to production.
• Consulting & Communication: Strong active listening, presentation, and storytelling skills; ability to confidently translate complex AI concepts for C-level stakeholders.
• AI & Agentic Tech Stack: Experience with RAG, LLM applications, multi-agent orchestration, and tools like LangChain, LangGraph, CrewAI, LlamaIndex, or Vector DBs.
• Data & Architecture: Understanding of Semantic Layers, metadata, knowledge graphs, and data platforms (such as Databricks or Snowflake).
• Engineering & MLOps: Familiarity with the production model lifecycle, MLOps/LLMOps pipelines, and cloud platform integrations.
   

Nice to Have

• Experience mentoring cross-functional AI teams or driving enterprise-scale GenAI adoption.
• Practical knowledge of cloud infrastructure and DevOps tooling.
• Insights into AI governance and ethical AI frameworks.

Необхідний досвід з навичками

Стабільна FinTech компанія з перспективними планами розвитку та зростання у пошуку Head of Data Science / Head of Credit Risk Analytics.

Продукт — онлайн-сервіс для кредитування, який продовжує доопрацьовуватись новими фічами.

Роль відповідає за побудову, розвиток та ефективність системи прийняття кредитних рішень, скорингових моделей і операційне управління кредитним ризиком. Забезпечує якість кредитного портфеля через розвиток моделей, аналітики та автоматизацію процесів прийняття рішень.

Обов'язки:

• Побудова, розвиток та підтримка автоматизованої системи прийняття кредитних рішень (Decision Engine);
• Розробка, імплементація, моніторинг та оптимізація скорингових моделей із використанням Machine Learning;
• Розробка, тестування та оптимізація Risk Rules, cut-off та лімітних політик;
• Аналіз кредитного портфеля, рівня дефолтів, ефективності скорингових моделей та ключових ризик-показників;
• Проведення Champion/Challenger та A/B тестування для оцінки ефективності моделей;
• Розробка та автоматизація аналітичної звітності для моніторингу кредитного ризику;
• Управління якістю даних та розвиток Data Warehouse;
• Інтеграція, оцінка та моніторинг ефективності зовнішніх джерел даних;
• Постановка технічних завдань команді розробки, тестування та контроль впровадження змін;
• Формування рекомендацій щодо оптимізації кредитної політики, підвищення якості портфеля та покращення бізнес-результатів;

• Взаємодія з Chief Risk Officer щодо впровадження затверджених ризик-політик у систему прийняття кредитних рішень.

   

Вимоги:

Обов'язкові:

• Досвід побудови та розвитку скорингових моделей;
• Досвід роботи у fintech, МФО або банківському секторі;
• Впевнене володіння Python;
• SQL (PostgreSQL, MS SQL);
• Практичний досвід використання Machine Learning;
• Досвід аналізу кредитного портфеля та управління кредитним ризиком;
• Знання статистики та методів аналізу даних;
• Досвід роботи з Decision Engine;
• Досвід розробки та підтримки Risk Rules;

• Досвід побудови аналітичної звітності та роботи з великими масивами даних.

   

Буде перевагою:

• Deep Learning;
• Excel Advanced;
• Power BI або Tableau;
• MLOps;
• Досвід роботи з альтернативними джерелами даних та antifraud-рішеннями.

Досвід роботи в мікрокредитній установі або банку – конкурентна перевага.

We are looking for a Quantitative Analyst to become the mathematical backbone of our Trading Core and Risk Tech squads. In a high-frequency trading environment processing billions in volume, success is defined by precision. 

In this role, you will design, backtest, and optimize the core mathematical models that drive our pricing engines, automated market-making algorithms, risk management frameworks (A/B/C-book optimization), and liquidation mechanics. You will sit at the intersection of advanced mathematics, data science, and high-performance software engineering, working directly with Core Product Managers and engineering teams to turn complex financial data into proprietary algorithmic advantages.

Responsibilities

• Research, design, and prototype quantitative models for pricing, risk management, and market making
• Build and maintain robust backtesting frameworks to validate the performance and safety of models before production deployment
• Write clear, comprehensive mathematical and algorithmic specifications for Backend Engineers (Trading Core squads)
• Cross functional with the R&D and Trading Ops teams
• Conduct post-incident deep dives (e.g., after major market gaps or liquidations) to identify algorithm performance gaps and optimize them

Requirements

• 4+ years of experience as a Quantitative Analyst / Researcher or Data Science
• Exceptional knowledge of probability theory, stochastic calculus, time-series analysis, and financial mathematics
• Advanced proficiency in Python (NumPy, Pandas, SciPy, SK-Learn, Statsmodels) for data analysis, modeling, and backtesting
• Experience with Machine Learning
• Deep understanding of market microstructure, order book dynamics, risk metrics (VaR, Expected Shortfall), and margin/liquidation mechanisms
• SQL skills and experience working with large-scale historical market data (tick data, order logs)
• Strong logical thinking, initiative, and well-developed communication skills

Will be a plus

• Experience in CFD, Crypto CEX, Prop Trading Firm, or Hedge Fund
• Degree (MSc or PhD preferred) in a highly quantitative field: Mathematics, Physics, Quantitative Finance, Statistics, or Computer Science
• Understanding of Asset pricing models (e.g., Black-Scholes, local volatility models, Greeks management)
• Knowledge of the MetaTrader platforms (MT4/MT5)
• Experience with AI (Claude.io, Copilot, Codex)

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

Who We Are

At Bennett Data Science, we've been pioneering the use of predictive analytics and data science for over a decade for some of the biggest brands and retailers. We're at the top of our field because we focus on delivering actionable AI for our clients. Our deep experience and product-first attitude set us apart from other groups and gets us the business results our clients want.

Why You Should Work With Us

You'll be exposed to a wide range of clients who are at the cutting edge of innovation in their field and get to work on fascinating problems, supporting real products, with real data. We help lots of companies, from some of the largest companies in the world to small startups in Silicon Valley who are building the next big thing.

Expert Mentorship: Direct guidance from senior staff with 20+ years of applied ML experience

Competitive Compensation: Market-rate pay with performance upside

Fully Remote: Work from any location of your choice, on a flexible schedule

The Role:
We are seeking a Senior Data Scientist to support the design, development, validation, and productionization of an advanced forecasting solution. This is a hands-on role requiring strong statistical judgment, practical modeling experience, and the ability to translate analytical findings into clear recommendations.

The successful candidate will work closely with data science, data engineering, and business stakeholders throughout the full modeling lifecycle, from exploratory analysis and model development to deployment, documentation, and handoff.

Key Responsibilities

• Develop, evaluate, and improve forecasting models using Python.
• Establish appropriate baselines and compare alternative modeling approaches.
• Design rigorous time-based backtesting and model-validation frameworks.
• Develop methods for handling limited historical data, changing patterns, and structural shifts.
• Produce prediction intervals and other measures of forecast uncertainty.
• Identify, test, and monitor potential forecast drivers.
• Develop configurable scenario-planning and sensitivity-analysis capabilities.
• Create model diagnostics and early-warning indicators.
• Write modular, tested, maintainable, and well-documented code.
• Collaborate with data engineering on production pipelines, model tracking, and automated forecast runs.
• Explain model results, assumptions, limitations, and changes to technical and non-technical stakeholders.
• Support technical documentation, knowledge transfer, and final solution handoff.
  
  Required Qualifications
• Five or more years of professional experience in data science, applied statistics, econometrics, forecasting, or a related field.
• Strong practical experience developing forecasting or predictive-modeling solutions.
• Advanced Python and SQL skills.
• Strong knowledge of regression, time-series methods, feature engineering, residual analysis, and uncertainty estimation.
• Experience designing rolling or time-based backtests and preventing data leakage.
• Ability to distinguish meaningful predictive signals from unstable or spurious correlations.
• Experience developing production-quality analytical code using version control and standard software-development practices.
• Strong analytical communication and stakeholder-management skills.

• Ability to work independently and deliver within a defined project timeline.
   

  Preferred Qualifications

• Experience developing scenario-planning or decision-support tools.
• Experience working with small historical samples or significant changes in underlying data patterns.
• Familiarity with Microsoft Fabric, Azure, MLflow, or comparable cloud-based analytics platforms.
• Experience productionizing forecasting models and implementing ongoing performance monitoring.
• Experience presenting analytical findings to Finance or senior leadership.