Lead Data Scientist

EPAM Systems; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

EPAM Systems Inc. seeks a Lead Data Scientist with deep knowledge of Large Language Models (LLMs) and Python to create AI-driven chatbot solutions for targeted use cases.

The role emphasizes NLP, Conversational AI, and responsible AI, with hands-on work in hallucination mitigation and compliance. Responsibilities include leading cross-functional teams, shaping prompt engineering, building LLM-driven agents, and guiding cloud infrastructure for deployments, while applying rigorous evaluation and

Qualifications

• 5+ years of hands-on data science or AI roles
• At least 1 year guiding and managing teams
• Strong command of LLMs with OpenAI APIs on Azure
• Advanced Python for AI workflows and integration
• Proven AI agents using function calling to connect tools/databases
• Solid NLP and Conversational AI experience
• English writing and speaking at B2 level or higher
• Nice to have experience with A/B testing and analytics teams

Responsibilities

• Collaborate with product, analytics, and engineering teams on chatbot design
• Fine-tune prompt engineering to improve conversations
• Build LLM-driven agents interfacing with APIs and databases via function calls
• Mitigate hallucinations with model evaluation methods
• Apply A/B testing to raise chatbot performance
• Embed Responsible AI and regulatory compliance throughout development
• Conduct EDA to extract insights for data engineering
• Advise on cloud infrastructure needs for AI deployments

Skills

LLMs

Python

NLP

Conversational AI

Model evaluation

Team leadership

Azure OpenAI

Tools

OpenAI API

Azure

Function calling

Cloud services

Job description

We are looking for a Lead Data Scientist with deep knowledge of Large Language Models (LLMs) and Python to create AI-driven chatbot solutions for targeted use cases. The ideal candidate brings a solid foundation in Natural Language Processing (NLP) and Conversational AI, along with hands‑on experience in hallucination mitigation, Responsible AI and compliance, and data analytics.

Responsibilities

• Team up with multidisciplinary groups spanning product, analytics, and engineering to design and improve a shopping assistant chatbot
• Shape and fine‑tune prompt engineering approaches to raise the quality of chatbot conversations
• Build LLM‑driven agents that interface with APIs, databases, and external services via function calling
• Protect chatbot dependability and reduce hallucinations by applying rigorous model evaluation methods
• Use A/B testing frameworks to assess and progressively elevate chatbot performance
• Embed Responsible AI and compliance principles to ensure ethical operation and regulatory conformity
• Carry out exploratory data analysis (EDA) to surface insights and convert them into well‑defined requirements for the data engineering team
• Advise cloud engineering teams on the infrastructure requirements essential to AI‑powered chatbot deployments

Requirements

• No less than 5 years of hands‑on professional experience in data science or applied AI positions
• At least 1 year of experience guiding and managing development teams
• Strong command of Large Language Models, including practical work with OpenAI APIs on Azure
• Advanced Python programming abilities for integrating, orchestrating, and refining AI workflows
• Track record of building AI agents that use function calling to engage with external tools and databases
• Solid grasp of hallucination prevention methods to preserve chatbot precision and dependability
• Firm understanding of Responsible AI and compliance best practices, along with ethical AI standards
• Demonstrated experience in Natural Language Processing (NLP) and Conversational AI creation
• Confident English communication skills at B2 level or above, in both writing and speech
• Nice to have Practical background in A/B testing and experimentation, working closely alongside analytics teams
• Skill in defining and executing a testing strategy grounded in key performance indicators specific to chatbot solutions
• Proficiency in exploratory data analysis (EDA) for drawing valuable insights from sizable datasets
• Aptitude for producing clear, structured data specifications for the data engineering team
• Familiarity with cloud services, whether through direct hands‑on work or by outlining infrastructure requirements for cloud engineering teams