Senior Data Scientist
Bristol Myers Squibb
16 days ago
Remote friendly (Cambridge, MA)
United States
IT
Summary
As a Senior Data Scientist within Bristol Myers Squibb's AI Venture Studio delivery team, you will convert ambiguous scientific and business opportunities into measurable AI product hypotheses, experiments, and working solutions. You will partner with AI, Data, App/Cloud, Frontend, product owners, and domain experts to build and evaluate AI systems across R&D, Commercialization, Manufacturing, and Enabling Functions.
Key Responsibilities
- Frame ambiguous business and scientific questions into measurable AI product hypotheses, success metrics, evaluation plans, and rapid experiments.
- Contribute to 6-sprint, 12-week AI Accelerator agile cycles by testing hypotheses, validating increments, and adapting analyses.
- Build prototypes using Python, SQL, notebooks, APIs, and AWS-aligned data services.
- Support sandboxed, non-production data problem solving to branch, transform, test, and audit code-plus-data experiments.
- Evaluate and curate analytical context (instructions, memory, tools, curated source meaning) and improve agent quality via measured impact.
- Partner with Data Engineers to shape datasets, retrieval corpora, metadata, and feature pipelines (S3, Athena, PostgreSQL/RDS, vector databases, knowledge graphs).
- Design/evaluate LLM, RAG, and agentic workflows (rubrics, golden datasets, structured validation, error taxonomies, hallucination risk, SME review loops) using tools such as LangGraph/LangSmith/PydanticAI.
- Define KPIs and measurement plans; apply experimental design/causal/quasi-experimental methods; communicate analyses, limitations, and recommendations.
- Contribute reusable evaluation harnesses and templates; participate in reviews and technical problem-solving; coach peers.
Qualifications & Experience
- BS+ in Data Science/Statistics/CS/Engineering/Bioinformatics/Computational Biology/Applied Mathematics or related.
- 5+ years in data science, machine learning, applied AI, analytics, or related roles.
- Proficiency in Python, SQL, R and libraries (pandas, NumPy, scikit-learn, PyTorch/TensorFlow, statsmodels).
- Experience with ML/statistics/NLP/IR/experimentation/decision science in real products or scientific/business workflows.
- Experience with LLM apps, RAG, agentic AI, prompt/evaluation design, structured outputs, context-quality evaluation, knowledge curation.
- Familiarity with AWS services (S3, Athena, RDS/PostgreSQL, OpenSearch, SageMaker, Bedrock) and vector databases/knowledge graphs/embeddings/metadata/data quality.
- Familiarity with Streamlit for prototyping; communicate findings to technical and non-technical audiences.
- Experience with coding agents/AI-assisted tools (e.g., Claude Code, Codex, Gemini CLI, GitHub Copilot).
- Excited to experiment with frontier AI while applying scientific rigor; curious mindset and ability to adapt in agile pods.
Application Instructions
- None stated in the provided text besides compensation/location/benefits details.
As a Senior Data Scientist within Bristol Myers Squibb's AI Venture Studio delivery team, you will convert ambiguous scientific and business opportunities into measurable AI product hypotheses, experiments, and working solutions. You will partner with AI, Data, App/Cloud, Frontend, product owners, and domain experts to build and evaluate AI systems across R&D, Commercialization, Manufacturing, and Enabling Functions.
Key Responsibilities
- Frame ambiguous business and scientific questions into measurable AI product hypotheses, success metrics, evaluation plans, and rapid experiments.
- Contribute to 6-sprint, 12-week AI Accelerator agile cycles by testing hypotheses, validating increments, and adapting analyses.
- Build prototypes using Python, SQL, notebooks, APIs, and AWS-aligned data services.
- Support sandboxed, non-production data problem solving to branch, transform, test, and audit code-plus-data experiments.
- Evaluate and curate analytical context (instructions, memory, tools, curated source meaning) and improve agent quality via measured impact.
- Partner with Data Engineers to shape datasets, retrieval corpora, metadata, and feature pipelines (S3, Athena, PostgreSQL/RDS, vector databases, knowledge graphs).
- Design/evaluate LLM, RAG, and agentic workflows (rubrics, golden datasets, structured validation, error taxonomies, hallucination risk, SME review loops) using tools such as LangGraph/LangSmith/PydanticAI.
- Define KPIs and measurement plans; apply experimental design/causal/quasi-experimental methods; communicate analyses, limitations, and recommendations.
- Contribute reusable evaluation harnesses and templates; participate in reviews and technical problem-solving; coach peers.
Qualifications & Experience
- BS+ in Data Science/Statistics/CS/Engineering/Bioinformatics/Computational Biology/Applied Mathematics or related.
- 5+ years in data science, machine learning, applied AI, analytics, or related roles.
- Proficiency in Python, SQL, R and libraries (pandas, NumPy, scikit-learn, PyTorch/TensorFlow, statsmodels).
- Experience with ML/statistics/NLP/IR/experimentation/decision science in real products or scientific/business workflows.
- Experience with LLM apps, RAG, agentic AI, prompt/evaluation design, structured outputs, context-quality evaluation, knowledge curation.
- Familiarity with AWS services (S3, Athena, RDS/PostgreSQL, OpenSearch, SageMaker, Bedrock) and vector databases/knowledge graphs/embeddings/metadata/data quality.
- Familiarity with Streamlit for prototyping; communicate findings to technical and non-technical audiences.
- Experience with coding agents/AI-assisted tools (e.g., Claude Code, Codex, Gemini CLI, GitHub Copilot).
- Excited to experiment with frontier AI while applying scientific rigor; curious mindset and ability to adapt in agile pods.
Application Instructions
- None stated in the provided text besides compensation/location/benefits details.