Vice President, Data Science and Machine Learning

The Position

Vice President, Data Science and Machine Learning

Position Overview

As the Vice President of Data Science and Machine Learning (DSML), you will lead Genentech’s enterprise-wide strategy to harness data science and machine learning (ML) in transforming how we discover insights, personalize customer experiences, and accelerate our mission to improve patient outcomes. Reporting to the SVP, Head of Data, Digital, and Analytics (DDA), you will shape and scale high-impact data science and ML capabilities that are deeply embedded in the decision-making, operations, and digital workflows across Commercial, Medical, and Government Affairs (CMG).

This role combines strategic leadership with deep technical stewardship. You will guide a world-class team of data scientists, ML engineers, and AI product managers while building strong partnerships within DDA and across CMG, Informatics, and Legal to ensure DSML innovation is responsible, compliant, and scalable. Your leadership will be central to positioning Genentech at the forefront of healthcare AI transformation.

Key Responsibilities:

• Define and execute a bold, enterprise-level data science and machine learning strategy aligned to Genentech’s mission and CMG goals.

• Serve as the strategic thought partner to CMG leadership to ensure data science is a core enabler of business growth, operational efficiency, and patient impact.

• Partner with DDA functional teams to develop a cross-functional product roadmap for data science, ML, predictive AI, genAI, agentic AI. 

• Partner with the Informatics (IX) team to develop a scalable, secure, and compliant ML/AI platform ecosystem — from experimentation to production deployment.

• Set the scientific and technical direction for the Data Science and ML function; lead the development of cutting-edge methodologies in areas such as predictive and prescriptive modeling, time-series and causal inference, graph ML and network science, natural language processing and LLM fine-tuning, generative and agentic AI systems, real-world evidence analytics and treatment simulation.

• Lead development of reusable AI/ML components and enterprise models across CMG domains such as HCP/patient engagement, forecasting, field force optimization, and real-world data analytics.

• Oversee architecture and technical strategy for end-to-end ML workflows, including data pipelines, feature stores, model training/validation/deployment, and performance monitoring.

• Partner with IX to ensure adoption of best-in-class MLOps practices to scale reproducible, automated, and compliant data science, ML, and AI development across use cases.

• Define and track success metrics (e.g., adoption, performance, ROI) for data science and machine learning products and platforms.

• Partner with Digital Experience and Informatics teams to integrate data science and AI into content generation, omnichannel engagement, CRM, and digital operations. Drive adoption of generative and agentic AI solutions to enhance speed, creativity, and personalization in business workflows.

• Contribute to enterprise AI governance policies and ethical frameworks to ensure responsible AI development and use.

• Lead strategic partnerships with academia, technology providers, and consortia to ensure Genentech remains at the forefront of AI innovation.

• Champion a culture of experimentation, agile execution, and continuous learning.

• Inspire, lead, and develop a high-performing team of data science experts. Foster a culture of respect, inclusion, growth mindset, and accountability. 

• Attract, develop, and retain top-tier talent in data science, ML engineering, and AI product management.; nurture the next generation of data science leaders.

Qualifications

• Master's degree in Data Science, Computer Science, Engineering, Statistics, or related field required; Ph.D.strongly preferred.

• 15+ years of progressive leadership experience in data science, AI/ML within healthcare, life sciences, or highly regulated industries.

• 10+ years of experience leading large teams, both in direct reporting as well as cross-functional groups.

• Deep expertise in data science, machine learning, statistical modeling, and AI platform technologies.

• Proven track record of driving AI/ML innovation from concept to scaled deployment, especially in complex