Director, Causal AI & Experimentation

Roche • South San Francisco, CA

4d • Hybrid

About The Position

Why Genentech We’re passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world’s most complex health challenges and transforming society. Genentech’s Data, Digital, and Analytics (DDA) team is dedicated to solving complex healthcare challenges and improving patient outcomes. DDA empowers business partners across Commercial, Medical, and Government Affairs (CMG) to make impactful decisions by leveraging data, analytics, and AI/ML to enable fast, targeted actions in rapidly evolving business contexts. DDA fosters a unified understanding of customers, actions, and outcomes by transforming the business insight supply chain from the traditional reactive service model to a modern proactive product model, which integrates analytics and insights seamlessly into CMG’s evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos. In DDA, you will work as a trusted, objective advisor and expert, recommending critical decisions and actions to be taken with credibility and a focus on driving measurable impact. You will be part of a diverse, inclusive team that reflects the world we serve, thriving in a welcoming culture built on collaboration and innovation. The Opportunity The Director, Causal AI and Experimentation leads and develops a high-performing team of data scientists, statisticians, and applied economists, driving the strategic application of Causal AI and data-driven Experimentation technologies within the CMG organization. This role focuses on fostering a data-driven culture, enabling and validating business impact through the development and integration of Causal AI and Experimentation capabilities. Responsible for building a highly connected and motivated team, this individual cultivates future leaders, provides mentorship, and oversees hiring efforts to ensure the team’s long-term success. This role drives cross-functional collaboration, partnering with key stakeholders to integrate data science solutions into decision-making processes. Define and execute the Causal AI & Experimentation strategy, focusing on advancing measurement capabilities to drive innovation and guide continuous improvement of data-driven business solutions. Act as a subject matter expert for applicable experimentation and measurement methodologies, including advanced Causal AI and emerging measurement technologies. Collaborate with data science product owners/managers, data leads, Machine Learning Engineers, Machine Learning Operations, and RDT teams to develop efficient data-driven applications, gain alignment, and deliver impactful business insights. Effectively communicate findings to both technical and non-technical audiences. Stay abreast of the latest advancements in data science and AI, particularly in Causal AI, ensuring responsible AI practices and applying innovative approaches to enhance AI product capabilities for measurement. Lead and mentor a team of data scientists, statisticians, and applied economists, fostering collaboration and supporting their professional development. Advocate AI adoption, partner with cross-functional teams for skill-building, foster data-driven decision-making, and build highly-connected, high-performing teams by leading, developing, and inspiring a thriving data science team.

Requirements

• Bachelor's degree in Statistics, Mathematics, Applied Economics or a related quantitative field.
• 8 years of experience with 5 years of experience as a Data Scientist or in a similar role with a track record of delivering successful data science products.
• Proficiency in programming languages such as Python, R.
• Strong knowledge of SQL for database management.
• Solid understanding of statistical methods and machine learning algorithms.
• Familiarity or hands-on experience with Causal AI and/or other industry-adopted measurement techniques, including but not limited to A/B testing, Market Mix Modeling, Observational Experimentation, etc.
• Excellent verbal and written communication skills, with the ability to present complex data analyses to non-technical stakeholders.
• Strong critical thinking and problem-solving abilities, with a detail-oriented approach to data analysis.

Nice To Haves

• Experience working with large and complex data sets in collaboration with business and analytics teams.
• Experience with deep learning, including Generative AI, frameworks.
• Contributions to open source projects or publications in data science, specifically in the Causal AI and/or experimentation/measurement domain.
• Experience in healthcare, pharmaceutical, or highly regulated industries.
• Relevant certifications in data science, machine learning, or AI technologies (e.g., Certified Analytics Professional, Microsoft Certified: Azure Data Scientist Associate.)

Responsibilities

• Define and execute the Causal AI & Experimentation strategy, focusing on advancing measurement capabilities to drive innovation and guide continuous improvement of data-driven business solutions.
• Act as a subject matter expert for applicable experimentation and measurement methodologies, including advanced Causal AI and emerging measurement technologies.
• Collaborate with data science product owners/managers, data leads, Machine Learning Engineers, Machine Learning Operations, and RDT teams to develop efficient data-driven applications, gain alignment, and deliver impactful business insights.
• Effectively communicate findings to both technical and non-technical audiences.
• Stay abreast of the latest advancements in data science and AI, particularly in Causal AI, ensuring responsible AI practices and applying innovative approaches to enhance AI product capabilities for measurement.
• Lead and mentor a team of data scientists, statisticians, and applied economists, fostering collaboration and supporting their professional development.
• Advocate AI adoption, partner with cross-functional teams for skill-building, foster data-driven decision-making, and build highly-connected, high-performing teams by leading, developing, and inspiring a thriving data science team.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees