Data Scientist

About Technical Operations

Bio Marin’s Technical Operations group is responsible for creating our drugs for use in clinical trials and for scaling production of those drugs for the commercial market. These engineers, technicians, scientists and support staff build and maintain Bio Marin’s cutting‑edge manufacturing processes and sites, provide quality assurance and quality control to ensure we meet regulatory standards, and procure the needed goods and services to support manufacturing and coordinating the worldwide movement of our drugs to patients.

TOPS acts as the critical link between Research & Development and Commercial Operations, integrating functions such as Technical Development, Manufacturing, Quality, Supply Chain, and Business Operations. Its teams maintain rigorous regulatory, quality, and safety standards while enabling seamless product advancement from development to market.

The organization operates with a strong culture of learning, continuous improvement, and data‑driven decision making. Through data integration, analytics, and digital transformation initiatives, TOPS enhances process monitoring, deviation analysis, optimization, and strategic capacity planning, ensuring accurate and actionable technical data to support operational excellence.

The Data Scientist in Technical Operations (TOPS) plays a critical role in advancing Bio Marin’s end‑to‑end product lifecycle by delivering high value Data/AI solutions across Technical Development, Manufacturing, Engineering, Quality, and Supply Chain functions.

Data Scientists in TOPS contribute to owning, developing and executing the organization’s Integrated Technical Data Strategy, applying advanced analytics, machine learning, and AI to complex datasets originating from Manufacturing, Quality and Supply Chain systems. They help transform fragmented data into actionable intelligence, extract insights which are otherwise hidden, identify gaps, and drive data maturity roadmap.

This role blends advanced technical skills in Data Science—covering statistics, Modelling, AI/ML—with deep domain expertise in a highly regulated Biotech industry. These should be complemented by soft skills including collaboration, clear communication, presentation skills, enhanced clarity and ability to effectively translate those requirements to solutions. Data Scientists are expected to collaborate across departments, partner with Business SMEs, other Data Scientists/Analysts/Engineers and IT, and lead initiatives that promote a culture focused on decision science with an end‑goal to help TOPS streamline operations, boost data reliability, and speed up decision‑making.

• Identify and frame AI opportunities across Technical Development, Manufacturing, Quality, and Supply Chain; translate ambiguous problems into tractable use cases with measurable outcomes.
• Maintain TOPS Data Science Portfolio of Projects. Participate in Portfolio prioritization, planning, solution design, development, and deployment.
• Lead Projects from start to finish by closely working with stakeholders, leadership and project team. Author business case, design, development and project implementation documents.
• Advance the Integrated Technical Data Strategy by defining roadmaps, value hypotheses, and success metrics that strengthen process robustness, speed, and cost/value realization.
• Acquire and prepare multi‑source technical data (e.g., MES, LIMS, QMS, ELN, SAP, PI), ensuring quality, lineage, and context for AI development at scale.
• Engineer domain‑aware features and reusable data assets that accelerate experimentation for manufacturing, quality, and supply analytics.
• Build and validate ML/AI models for use cases such as process monitoring, anomaly/root‑cause analysis, yield and cycle‑time optimization, and intelligent document processing.
• Develop GenAI solutions (e.g., RAG for SOPs/reports, Semantic search, Q&A assistants over technical data, workflow copilots) using approved enterprise platforms.
• Operationalize models (MLOps) with reproducible pipelines by closely working with Data Engineering team—data ingestion, training, evaluation, versioning, deployment—and monitor…