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Senior Data Scientist
$137,530–$183,319 year
On-site · Seattle, Washington, United States or Princeton, New Jersey, United States
Job Summary
Senior Data Scientist in BMS's AI Venture Studio delivery team who translates ambiguous scientific and business opportunities into measurable AI product hypotheses and working solutions. You will partner with AI Engineers, Data Engineers, App/Cloud Engineers, Frontend Engineers, product owners, and domain experts to build and evaluate AI systems across R&D, Commercialization, Manufacturing, and Enabling Functions. Responsibilities include designing evaluation datasets, prototyping models, testing agent and retrieval performance, measuring product impact, and explaining model behavior to stakeholders. You will define analytical context for agents, curate data tools and source material, and evaluate context quality, memory, and metadata. You will work with AWS-aligned data services and enterprise tools (e.g., OpenSearch, S3/Knows, PostgreSQL/RDS, vector databases, knowledge graphs) and apply rigorous evaluation methodologies to ensure trust and reliability in AI products. You will define KPIs and measurement plans, conduct bi-weekly demos, and collaborate across teams to deliver scalable AI solutions. You will contribute reusable notebooks, evaluation harnesses, and templates, participate in code and design reviews, and coach peers in practical data science and evidence-based decision making. Compensation is location-dependent and includes benefits such as health coverage, 401(k), and flexible time off; on-site/remote-work models are defined per role with site-essential or hybrid options as described in BMS occupancy guidelines.
Required Qualifications
- Bachelor's degree or higher in Data Science, Statistics, Computer Science, Engineering, Bioinformatics, Computational Biology, Applied Mathematics, or a related scientific field
- 5+ years of experience in data science, machine learning, applied AI, analytics, computational science, or related technology roles
- Proficiency in Python, SQL, R and/or pandas, NumPy, scikit-learn, PyTorch, TensorFlow or similar libraries
- Experience applying machine learning, NLP, information retrieval, experimentation, or decision science to real-world products or scientific/business workflows
- Experience with LLM applications, retrieval augmented generation (RAG), agentic AI, prompt/evaluation design, structured outputs, context-quality evaluation, knowledge curation, and model quality assessment
- Familiarity with AWS data and AI services (S3, Athena, RDS/PostgreSQL, OpenSearch, SageMaker, Bedrock) or equivalent cloud tools
- Experience with evaluation rubrics, hallucination risk measurement, causal inference, simulation, optimization, and reusable evaluation harnesses
- Familiarity with vector databases, knowledge graphs, embeddings, metadata strategy, and data quality practices
- Experience communicating quantitative findings to technical and non-technical audiences
- Excitement for experimenting with the latest AI tools and technologies in agile environments
- Ability to work in fast-moving AI delivery environments and adapt analyses as evidence evolves
- Strong collaboration and coaching abilities
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