AI Practice Lead - Life Sciences & Healthcare

About the role

EPAM AI consulting practice focuses on unlocking business value for organizations through end-to-end AI/ML solutions and platforms. From advanced analytics, marketing intelligence, and NLP to Responsible AI and MLOps, EPAM has delivered hundreds of solutions to production. We are looking for an AI leader to spearhead the initiatives across our Life Sciences and Healthcare Portfolio. This is a role where you will oversee the entire lifecycle of AI solutions, from ideation through delivery and growth, in step with technology and market trends. The ideal candidate thrives in ambiguity and can continually adapt to ensure their contributions make a tangible impact in the world.

Responsibilities

• Work with EPAM’s clients and EPAM internal teams (business SME’s, account management teams, etc.) to shape the AI/ML/Agentic business and technology vision and GTM offerings aligned with current and emerging needs
• Manage end-to-end Data Science, AI, and Agentic projects/programs
• Lead multidisciplinary workshops and design sessions with customers
• Develop high-quality proposals in conjunction with other SMEs and consultants
• Work on EPAM’s Data Science, AI, and Agentic offerings from a point of view
• Take an active part in growing the EPAM team’s capacity and capability including building new competencies, selecting, and mentoring new team members
• Acts as an agent of change internally and a champion of transformation with external stakeholders

Requirements

• 10+ years of experience as Data Science Lead, Machine Learning Engineering Lead, or similar roles in the Healthcare, Pharmaceutical, MedTech/BioTech space
• 5+ years in a leadership role with team management responsibilities
• Demonstrated expertise in one or more areas of the life sciences or healthcare value chain (e.g., R&D, Clinical, Commercial, Manufacturing, Payer, or Provider), pharma manufacturing
• Great communication skills: ability to communicate AI and LSHC concepts compellingly to a variety of audiences with ease
• Hands-on experience, focused in at least one of the following domains: computer vision, NLP/language models, recommender systems, time series analytics
• Machine Learning Engineering/Production delivery experience, ML/LLMOps. Experience working with one or more major cloud providers
• Knowledge of one or more of the following technologies: Databricks, Snowflake, Agentic Orchestration (LangChain, Omnigent, Semantic Kernel, etc.)
• Working knowledge of at least one programming language, ideally Python
• Understanding of Data Science engineering excellence and the modern SDLC for AI products
• Consulting experience (internal or through a professional services firm)

Nice to have

• Currently acting as an AI leader for a healthcare or life science organization
• Referenceable work (papers, open-source contributions, conference talks) around the AI / ML space
• Current cloud certifications for at least one of: AWS, GCP, Azure, Databricks
• Experience navigating regulatory processes, ISO/IEC Standards, HIPAA, EU AI Act, GxP, etc.

Benefits

• Medical, Dental and Vision Insurance (Subsidized)
• Health Savings Account
• Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
• Short-Term and Long-Term Disability (Company Provided)
• Life and AD&D Insurance (Company Provided)
• Employee Assistance Program
• Unlimited access to LinkedIn learning solutions
• Matched 401(k) Retirement Savings Plan
• Paid Time Off – the employee will be eligible to accrue 15–25 paid days, depending on specific level and tenure with EPAM (accrual eligibility may change over time)
• Paid Holidays – nine (9) total per year
• Legal Plan and Identity Theft Protection
• Accident Insurance
• Employee Discounts
• Pet Insurance
• Employee Stock Purchase Program
• If otherwise eligible, participation in the discretionary annual bonus program
• If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program

Pay

Salary range: $200,000 – $250,000 (base salary only). Individual compensation offers within the range are based on a variety of factors, including geographic location, experience, credentials, education, training, the demand for the role, and overall business and labor market considerations.