Job Description

Build and lead the technical delivery of AI applications within Vi’s Life Science Markets squad. The squad turns large, varied proprietary datasets into products that help evaluate pharmaceutical markets, treatment adoption, competitive dynamics, and drug commercial performance.

This is primarily a hands-on role. You will spend most of your time designing systems, writing production code, integrating models, and shipping AI workflows, while owning technical standards and overall squad delivery. A background in life sciences or capital markets is very helpful, but not required.

What You’ll Own

• Hands-on delivery: Personally build and ship core AI services and workflows. Own critical implementation work and resolve technical blockers alongside engineers, data scientists, and domain experts.
• Squad leadership & management: Lead the engineers in the squad and the overall execution. Set implementation standards through your own work, review code and architecture, and coordinate technical priorities and dependencies.
• Strategy and roadmap: Identify the life sciences market questions where Vi’s data can add value. Prioritize use cases, define success measures, and turn them into a focused roadmap.
• AI architecture and implementation: Design and implement systems combining ML models, LLMs, retrieval, and workflow automation to address life sciences market questions. Make practical choices about model quality, reliability, latency, and cost.
• Engineering and evaluation quality: Build automated tests, evaluation datasets, and monitoring for AI outputs and application reliability. Maintain rigorous validation of predictive models, including point-in-time backtesting, baseline comparisons, and checks for leakage and coverage bias.
• Production ownership: Take prototypes into production and own models, APIs, integrations, deployment, observability, and incident resolution. Build reusable capabilities that scale across therapies, companies, and clients.
• Client partnership & business logic: Work directly with clients and domain experts to understand their analytical workflows, explain findings, challenge assumptions, and turn feedback into product priorities.

What We're Looking For

• A strong record of personally building and shipping production AI applications, with technical ownership of complex systems and measurable business outcomes.
• Experience leading technical delivery across functions while remaining a primary contributor to design, modeling, coding, debugging, and deployment.
• Deep practical experience integrating ML models and LLMs into production systems, including retrieval, tool use, structured outputs, and evaluation. Ability to assess model limitations and choose methods suited to the available data
• Client-facing engineering: You can run a working session with a client’s technical and non-technical people and come out of it with what will move the needle for them. Ability to translate complex analysis into clear conclusions for senior stakeholders and clients, including what the evidence cannot support..
• Hands-on depth in scalable data pipelines, automated testing, CI/CD, and cloud infrastructure, ideally on AWS. Comfortable owning both application code and AI deployment.
• Experience turning bespoke analyses or pilots into products used repeatedly by multiple clients or teams.
• A background in life sciences, pharmaceutical analytics, or capital markets is very helpful, but not required.