Data Scientist 2

BioMarin Pharmaceutical Inc. (Swiss Office) seeks a mid-level Data Scientist 2 for on-site work in Switzerland. The role focuses on applying advanced analytics, machine learning, and AI to technical operations across manufacturing, quality, and supply chain.

This position supports BioMarin’s Technical Operations by delivering high-impact data and AI solutions throughout the product lifecycle. The Data Scientist helps execute the organization’s integrated technical data strategy, using advanced analytics and machine learning to solve complex challenges in manufacturing, quality, and supply chain systems. The role requires blending technical expertise in data science with a deep understanding of the regulated biotech industry to drive decision science and operational efficiency.

Responsibilities

• Identify and define AI opportunities across technical development, manufacturing, quality, and supply chain, translating ambiguous challenges into measurable use cases.
• Manage the Data Science project portfolio, including prioritization, planning, solution design, development, and deployment.
• Lead projects from inception to completion, collaborating with stakeholders and leadership while authoring business cases and implementation documents.
• Develop roadmaps, value hypotheses, and success metrics to advance the integrated technical data strategy and improve process robustness and cost efficiency.
• Acquire and prepare multi-source technical data from systems like MES, LIMS, QMS, ELN, SAP, and PI, ensuring quality and context for AI development.
• Engineer domain-aware features and reusable data assets to accelerate experimentation in manufacturing, quality, and supply analytics.
• Build and validate ML/AI models for process monitoring, anomaly detection, yield optimization, cycle-time improvement, and intelligent document processing.
• Develop Generative AI solutions, such as RAG for SOPs, semantic search, Q&A assistants, and workflow copilots, using approved enterprise platforms.
• Operationalize models through MLOps practices, including reproducible pipelines, data ingestion, training, evaluation, versioning, deployment, and continuous monitoring for drift and performance.
• Collaborate with IT and engineering to ensure scalable, secure, and supportable AI services aligned with platform standards.
• Create clear data visualizations and dashboards that communicate model insights to engineers, operators, quality leads, and executives.
• Champion data integrity and documentation, including model cards and validation records, consistent with regulated biotech practices.
• Educate and enable partners through demos, playbooks, and training to raise data and AI literacy across technical operations functions.
• Quantify and report value realization, such as cost avoidance, OEE improvements, and cycle-time reduction, while maintaining a transparent backlog of AI initiatives.
• Promote a 'build-first' approach using internal platforms before third-party tools when requirements are met internally with better agility and cost efficiency.
• Contribute to AI standards, including feature stores, evaluation frameworks, and prompt/agent guidelines, and mentor peers to strengthen the data science community.
• Stay current on AI advances like foundation models, time-series analysis, causal inference, and digital twins, assessing their applicability to technical operations use cases.

Requirements

• Master’s degree in Data Science, Computer Science, Statistics, or a related field, plus 5+ years of hands-on experience delivering data and AI solutions in an industry setting.
• Advanced proficiency in SQL and Python for data wrangling, feature engineering, modeling, and automation.
• Strong experience with databases (Postgres, SQL Server) and data platforms (Azure Databricks).
• Proven track record of managing end-to-end data analysis projects, from problem definition and requirements gathering to data validation and results presentation.
• Proficiency in enterprise Business Intelligence tools such as Power BI, Tableau, or Spotfire.
• Solid understanding of data modeling principles and design patterns.
• Experience building and operationalizing GenAI pipelines on Databricks, including chunking, RAG, vector indexing, Delta, Unity Catalog, MLflow, Jobs/Workflows, Spark, and Lakeflow.
• Working knowledge of Microsoft Azure, including storage, compute, identity/governance, and Azure OpenAI.
• High-level understanding of data engineering pipelines and data quality practices.
• Experience extracting and structuring data from unstructured sources (SOPs, reports, PDFs, ELN entries) using NLP or GenAI.
• Demonstrated experience in biotech/biopharma operations and partnering with subject matter experts across technical development, manufacturing, quality, or supply.
• Familiarity with Computer System Validation (CSV) documentation practices in regulated environments.
• Strong communication skills to support collaboration across technical development, manufacturing, quality, and supply chain functions.

Nice to have

• Experience developing Python-based web applications using frameworks like Dash, Flask, or Streamlit. Familiarity with HTML/CSS and TypeScript frameworks (React) is a plus.

About the company

BioMarin is a global biotechnology company dedicated to translating genetic discoveries into new medicines that advance human health. Since its founding in 1997, the company has leveraged expertise in genetics and molecular biology to create transformative treatments for patients with significant unmet medical needs. BioMarin fosters an environment that empowers teams to pursue bold, innovative science, resulting in a diverse pipeline of candidates with well-understood biology and the potential to be first-to-market or offer substantial benefits over existing therapies.

• Relentlessly pursues bold science to translate genetic discoveries into new medicines.
• Applies scientific expertise in understanding the underlying causes of genetic conditions.
• Develops medicines for patients with significant unmet medical need.
• Enlists top talent with technical expertise and a drive to solve real problems.
• Creates an environment that empowers teams to pursue bold, innovative science.
• Produces a diverse pipeline of commercial, clinical, and preclinical candidates.
• Focuses on first-to-market opportunities or substantial benefits over existing therapeutic options.