Senior GenAI Data Scientist

Join Liberty Mutual as an Analyst II, Data Science, focused on GenAI, on the Service Data Science team within US Retail Markets Insights & Solutions organization, and help shape how millions of customers and agents interact with insurance.

You will support GenAI projects and partner with business as well as technology partners on implementation. Examples of projects include using service call transcripts to identify customer pain points and analyze customer sentiment; building chat bot across customer service needs; creating real-time service guidance; and implementing auto text fill from documents etc. It’s a great opportunity to learn, grow, and see your work drive measurable business outcomes.

**This role may have in-office requirements based on candidate location**

**Level of position offered will be based on skills and experience at manager discretion**

Responsibilities:

• Contribute to GenAI projects from design to production implementation, independently or in partnership with other data scientists, depending on complexity of projects.
• Build POCs (proof of concept) and visualizations to socialize ideas.
• Mine large datasets to generate insights to inform business decisions.
• Communicate effectively with stakeholders to understand requirements, provide updates, and articulate value.
• Collaborate closely with technology and operations teams to ensure seamless deployment and integration of GenAI models into production.
• Create opportunity sizing based on business impact and create alignment with stakeholders.
• Assess third-party Gen AI vendor solutions for potential adoption.
• Create data engineering workflow for cleaning, dataset curation, augmentation.
• Follow ML Ops best practices to create organized code repos, production-quality code, and reproducible results.
• Coach and mentor junior data scientists and foster a culture of continuous learning.
• Regularly engage with the data science community, share knowledge, learn best practices and participate in cross functional working groups.

The ideal candidate will have: 

• Practical experience with foundation models (such as OpenAI/GPT, Llama derivatives etc) and prompting engineering (such as few-shot or chain-of-thought)
• Hands on experience with LLM tools such for orchestration (e.g. Langchain, etc.), vectordb stores (e.g. ChromaDB, Postgres, etc.)
• Familiarity with RAG and experience implementing and evaluating RAG pipelines
• Experience with GenAI evaluation and metrics
• Experience with software engineering best practices: version control, testing etc
• Proficiency in Python
• Familiarity with deep learning architectures and training dynamics with practical experience

• Solid knowledge of predictive analytics techniques and statistical diagnostics of models.
• Foundational/intermediate knowledge of predictive toolset; expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Has a value-driven perspective with regard to understanding of work context and impact.
• Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and no professional experience, a Master`s degree (scientific field of study) and 2 to 3 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and 4+ years of relevant experience.