Description

Join Vi’s Life Science Markets squad that turns large, varied proprietary datasets into products that help evaluate pharmaceutical markets, treatment adoption, competitive dynamics, and drug commercial performance.

You will design, build, and deploy AI-powered applications, services, and data workflows, working hands-on with engineers, data scientists, and domain experts. You will own implementation from prototype through production. A background in life sciences or capital markets is very helpful, but not required.

What You'll Own

• Applied AI development: 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 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 track record of independently shipping applied AI applications or services, from initial prototype to production use.

• Practical experience integrating ML models and LLMs into software, including retrieval, tool use, structured outputs, and evaluation. Sound judgment about model limitations and when a simpler approach is sufficient.

• 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.

• Strong software engineering fundamentals: API design, testing, version control, and maintainable code.

• Experience deploying and operating AI systems, including CI/CD, model versioning, monitoring, and troubleshooting.

• Ability to work with large datasets in the cloud, ideally using AWS services such as S3, Glue, EMR, MWAA, and SageMaker.

• Sound judgment with incomplete or noisy data, clear communication, and the ability to work independently within a cross-functional squad & client stakeholders.

• A background in life sciences, pharmaceutical analytics, or capital markets is very helpful, but not required.