Sr. Data Scientist 1
BioMarin Pharmaceutical Inc.
August 31, 2026
Remote friendly (Novato, CA)
United States
$143,200 - $196,900 USD yearly
IT
Responsibilities:
- Identify and frame AI opportunities across Technical Development, Manufacturing, Quality, and Supply Chain; translate problems into measurable use cases.
- Own and maintain the TOPS Data Science project portfolio: prioritization, planning, solution design, development, and deployment.
- Lead projects end-to-end with stakeholders; author business cases and implementation documents.
- Advance the Integrated Technical Data Strategy (roadmaps, value hypotheses, success metrics).
- Acquire/prepare multi-source technical data (e.g., MES, LIMS, QMS, ELN, SAP, PI) with quality, lineage, and context.
- Engineer domain-aware features and reusable data assets.
- Build/validate ML/AI models for process monitoring, anomaly/root-cause, yield/cycle-time optimization, and intelligent document processing.
- Develop GenAI solutions (RAG, semantic search, Q&A, workflow copilots) on approved platforms.
- Operationalize models using MLOps pipelines; monitor drift/performance/data quality.
- Collaborate with IT/Engineering for secure, scalable AI services.
- Deliver decision support via dashboards and narratives.
- Champion data integrity/documentation (model cards, validation records).
- Enable adoption via demos/playbooks/training; quantify value realization and manage an AI initiative backlog.
- Stay current on AI advances and assess applicability; contribute to TOPS AI standards; mentor peers.
Qualifications:
- Master’s (minimum) in Data Science/CS/Statistics or related; 7+ years delivering Data/AI solutions.
- Advanced SQL and Python; web apps (Dash/Flask/Streamlit) and React/TS a plus.
- Databases (Postgres/SQL Server) and Azure Databricks.
- Proven end-to-end data project delivery.
- BI tools (Power BI/Tableau/Spotfire).
- GenAI pipelines on Databricks (e.g., chunking, RAG, vector indexing; Delta/Unity Catalog/MLflow).
- Azure knowledge (incl. Azure OpenAI) and data quality practices.
- Experience with unstructured data extraction (NLP/GenAI).
- Biotech/biopharma operations + SMEs; familiarity with Computer System Validation (CSV).
- Strong communication across functions.
- Identify and frame AI opportunities across Technical Development, Manufacturing, Quality, and Supply Chain; translate problems into measurable use cases.
- Own and maintain the TOPS Data Science project portfolio: prioritization, planning, solution design, development, and deployment.
- Lead projects end-to-end with stakeholders; author business cases and implementation documents.
- Advance the Integrated Technical Data Strategy (roadmaps, value hypotheses, success metrics).
- Acquire/prepare multi-source technical data (e.g., MES, LIMS, QMS, ELN, SAP, PI) with quality, lineage, and context.
- Engineer domain-aware features and reusable data assets.
- Build/validate ML/AI models for process monitoring, anomaly/root-cause, yield/cycle-time optimization, and intelligent document processing.
- Develop GenAI solutions (RAG, semantic search, Q&A, workflow copilots) on approved platforms.
- Operationalize models using MLOps pipelines; monitor drift/performance/data quality.
- Collaborate with IT/Engineering for secure, scalable AI services.
- Deliver decision support via dashboards and narratives.
- Champion data integrity/documentation (model cards, validation records).
- Enable adoption via demos/playbooks/training; quantify value realization and manage an AI initiative backlog.
- Stay current on AI advances and assess applicability; contribute to TOPS AI standards; mentor peers.
Qualifications:
- Master’s (minimum) in Data Science/CS/Statistics or related; 7+ years delivering Data/AI solutions.
- Advanced SQL and Python; web apps (Dash/Flask/Streamlit) and React/TS a plus.
- Databases (Postgres/SQL Server) and Azure Databricks.
- Proven end-to-end data project delivery.
- BI tools (Power BI/Tableau/Spotfire).
- GenAI pipelines on Databricks (e.g., chunking, RAG, vector indexing; Delta/Unity Catalog/MLflow).
- Azure knowledge (incl. Azure OpenAI) and data quality practices.
- Experience with unstructured data extraction (NLP/GenAI).
- Biotech/biopharma operations + SMEs; familiarity with Computer System Validation (CSV).
- Strong communication across functions.