Principal Data Scientist

Genentech • Daly City, CA

9d • $217,400 - $403,700 • Hybrid

About The Position

The Principal Data Scientist - Causal AI & Experimentation is a technical leader and key domain authority for advanced experimentation, measurement, and optimization across the CMG organization. This high-impact individual-contributor role bridges causal methodology and business strategy by driving the design, development, and industrialization of enterprise-grade causal AI and statistical models used to measure business impact, optimize resource allocation, and support data-driven decisions.

Working alongside data scientists and engineers, the role helps shape technical strategy for measurement and experimentation initiatives. It also provides mentorship, establishes modeling standards, and partners with cross-functional teams to deliver scalable AI solutions. The position is based in South San Francisco with a hybrid schedule requiring three days per week on campus. Relocation benefits are not offered.

Requirements

• Bachelor’s degree and at least 8 years of work experience, or an equivalent combination of education and work experience.
• At least 8 years of relevant industry experience in Data Science, Applied Statistics, or ML Engineering, with a track record of architecting and shipping impactful data science products.
• At least 5 years of hands-on specialization in causal inference, experimental design, measurement, and optimization, such as A/B testing, quasi-experiments, observational causality, Market Mix Modeling, Bayesian modeling, or uplift modeling.
• Expert-level knowledge of open-source ML, statistics, and causal inference frameworks such as PyTorch, TensorFlow, scikit-learn, statsmodels, CausalML, DoWhy, or EconML.
• Advanced proficiency in Python and/or R and strong SQL skills for complex database management and data manipulation.
• Deep familiarity with open-source measurement packages such as Robyn, LightweightMMM, or Meridian.
• Hands-on experience scaling algorithms with big data environments such as Spark, Databricks, or Hadoop and cloud platforms such as AWS, GCP, or Azure.
• Exceptional verbal and written communication skills, including the ability to influence partners, align business priorities, and explain complex mathematical methods to business leaders.
• Strong critical thinking and rigorous attention to experimental design, bias mitigation, and causal validity.

Nice To Haves

• Advanced degree (MS or PhD preferred) in Statistics, Mathematics, Applied Economics, Computer Science, Data Science, or a related quantitative field focused on AI/ML or Causal Inference.
• Experience working with complex, large-scale datasets in healthcare, pharmaceuticals, or highly regulated environments, including longitudinal patient-level, syndicated sales, or promotional data.
• Familiarity or hands-on experience integrating Deep Learning or Generative AI techniques with Causal AI frameworks.
• Contributions to open-source data science tools or peer-reviewed publications in causal inference, experimentation, or measurement technology.
• The preferred qualifications also mention 6+ years of relevant industry experience and 6 years with a PhD.

Responsibilities

• Drive technical design and methodology for Causal AI, measurement, and experimentation projects, translating business objectives into mathematical and algorithmic frameworks.
• Design, build, and deploy production-grade statistical, econometric, machine learning, and causal inference models, including A/B and quasi-experimentation, Media/Marketing Mix Modeling, synthetic controls, uplift modeling, and non-parametric measurement.
• Develop scalable algorithms to quantify the causal impact of promotional and non-promotional activities and generate actionable business optimization recommendations.
• Partner with business and technical product managers, data engineers, ML engineers, MLOps, and commercial stakeholders to translate priorities into technical requirements and industrialize Causal AI applications end to end.
• Provide technical guidance as a Causal AI subject matter expert, conduct code and design reviews, and mentor team members.
• Explain causal modeling and statistical concepts clearly and translate them into actionable guidance for technical teams and business leaders.
• Track advances in Causal AI, Causal ML, Bayesian modeling, and Generative AI; advocate responsible AI practices and bring novel approaches into the measurement ecosystem.

Benefits

• A discretionary annual bonus may be available based on individual and Company performance.
• The position qualifies for company benefits detailed at a link in the posting; specific benefits are not listed.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal