Lead Data Scientist

We are looking for a Lead Data Scientist to convert intricate data into impactful insights and machine learning solutions that drive key business outcomes.

You will work closely with product and engineering teams to define challenges, develop models, and present clear data-driven stories. Bring your expertise in dashboarding tools like Salesforce Tableau and Microsoft Power BI, alongside cloud platforms such as Snowflake, to influence strategic decisions and foster innovation.

Responsibilities

• Convert business goals into precise data science challenges with clear metrics for success
• Investigate, cleanse, and merge complex datasets while crafting significant features
• Develop, assess, and enhance predictive, classification, ranking, NLP, and time-series models
• Plan and evaluate experiments including A/B and multi-arm tests to ensure dependable causal conclusions
• Present insights through compelling narratives, visual aids, and practical recommendations
• Work alongside product and engineering teams to deploy and oversee machine learning models in production environments
• Create and sustain scalable dashboards and reports for real-time business tracking
• Design and improve data pipelines for large-scale data ingestion, transformation, and integration
• Establish, execute, and monitor success indicators for AI-based features and digital transformation initiatives
• Collaborate with stakeholders to translate data findings into strategic business actions

Requirements

• Proven data science expertise with 5+ years in production settings
• Solid grounding in statistics, probability theory, and experimental design
• High proficiency in SQL and at least one programming language with machine learning libraries
• Experience managing large datasets and working with distributed data processing systems
• Strong skills in model evaluation, validation, and monitoring using offline and online metrics
• Ability to create data visualizations and communicate effectively with executives
• Understanding of MLOps practices and teamwork with data and platform engineering groups
• Bachelor’s or Master’s degree in a quantitative discipline or equivalent experience
• Upper-Intermediate English language ability (B2)

Nice to have

• Background in natural language processing, forecasting, recommendation systems, or anomaly detection
• Knowledge of causal inference and A/B testing techniques
• Practical experience with feature stores, real-time data processing, or online ML systems
• Familiarity with cloud data platforms and contemporary data warehouses
• Experience mentoring others and contributing to cross-functional projects

We offer

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• Employee financial programs
• Paid time off and sick leave
• Upskilling, reskilling and certification courses
• Unlimited access to the LinkedIn Learning library and 22,000+ courses
• Global career opportunities
• Volunteer and community involvement opportunities
• EPAM Employee Groups
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn