Principal Data Scientist - Causal AI & Experimentation

About the role

The Opportunity

As a Principal Data Scientist - Causal AI & Experimentation (within our Data Science and Machine Learning team), you will serve as a technical leader and key domain authority for advanced experimentation, measurement, and optimization solutions across the CMG organization. In this high-impact, individual-contributor role, you will bridge the gap between cutting-edge causal methodology and high-stakes business strategy. You will drive the design, development, and industrialization of enterprise-grade causal AI and statistical models that measure business impact, optimize resource allocation, and guide data-driven decision-making.

Working alongside other data scientists and engineers, you will help shape the technical strategy for measurement and experimentation initiatives. You will serve as a mentor to senior and junior data scientists, establish best-in-class modeling standards, and partner closely with cross-functional teams to deliver scalable AI solutions.

Key Responsibilities

Drive the technical design and methodology for key Causal AI, measurement, and experimentation projects, translating complex business objectives into robust mathematical and algorithmic frameworks.

Design, build, and deploy production-grade statistical, econometric, machine learning, and causal inference models (e.g., A/B & quasi-experimentation, Media/Marketing Mix Modeling, synthetic controls, uplift modeling, and non-parametric measurement).

Develop scalable algorithms to quantify the true causal impact of promotional and non-promotional activities, generating actionable recommendations for business optimization.

Partner closely with business and technical product managers, data engineers, ML engineers, MLOps, and commercial stakeholders to translate business priorities into scalable technical requirements and drive the end-to-end industrialization of Causal AI applications.

Act as a technical authority and subject matter expert (SME) in Causal AI. Provide technical guidance, perform code and design reviews, and mentor team members to foster technical excellence and continuous learning.

Translate complex causal modeling and statistical concepts into clear, compelling narratives and actionable guidance for both technical teams and non-technical business leaders.

Stay at the forefront of academic and industry advancements in Causal AI, Causal ML, Bayesian modeling, and Generative AI, advocating for responsible AI practices and bringing novel approaches into our measurement ecosystem.

Who You Are

You have aA Bachelor’s Degree and 8+ years of work experience, or an equivalent mix of education and work experience, is required.

You have 8+ years of relevant industry experience in Data Science, Applied Statistics, or ML engineering with a proven track record of architecting and shipping impactful data science products.

5+ years of hands-on specialization in Causal Inference, Experimental Design, Measurement, and Optimization (e.g., A/B testing, quasi-experiments, observational causality, Market Mix Modeling, Bayesian modeling, uplift modeling)

Expert-level knowledge of open-source frameworks for ML, statistics, and causal inference (e.g., PyTorch, TensorFlow, scikit-learn, statsmodels, CausalML, DoWhy, EconML).

Advanced proficiency in Python and/or R, along with strong SQL skills for complex database management and data manipulation

Deep familiarity with open-source measurement packages (e.g., Robyn, LightweightMMM, Meridian).

Hands-on experience scaling algorithms using big data environments (Spark, Databricks, Hadoop) and cloud platforms (AWS, GCP, or Azure).

Exceptional verbal and written communication skills with a demonstrated ability to influence key partners, align business priorities, and explain complex mathematical methodologies to business leaders.

Strong critical thinking abilities with rigorous attention to detail regarding experimental design, bias mitigation, and causal validity.

Preferred Qualifications

Advanced degree (MS or PhD preferred) in Statistics, Mathematics, Applied Economics, Computer Science, Data Science, or a related quantitative field with a focus on AI/ML or Causal Inference with 6+ years of relevant industry experience in Data Science, Applied Statistics, or ML engineering 6+ years with a PhD

Experience working with complex, large-scale dataset structures in healthcare, pharmaceuticals, or highly regulated environments (including longitudinal patient-level data, syndicated sales, or promotional data).

Familiarity or hands-on experience integrating Deep Learning or Generative AI techniques with Causal AI frameworks.

A history of contributions to open-source data science tools or peer-reviewed publications in causal inference, experimentation, or measurement technology.

Location

This role is based in South San Francisco and offers a hybrid schedule working 3 day per week on campus

Relocation benefits are NOT offered on this posting

The expected salary range for this position based on the primary location of South San Francisco, CA is $217,400/yr to $403,700/yr. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

#LI-CM4

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.