Principal Data Scientist

Roche • South San Francisco, CA

9d • $217,400 - $403,700 • Hybrid

About The Position

As a Principal Data Scientist - Causal AI & Experimentation within the 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 connect causal methodology with business strategy and drive the design, development, and industrialization of enterprise-grade causal AI and statistical models to measure business impact, optimize resource allocation, and support data-driven decisions.

You will work alongside data scientists and engineers to shape technical strategy for measurement and experimentation initiatives. You will mentor data scientists, establish modeling standards, and partner with cross-functional teams to deliver scalable AI solutions.

Requirements

• Bachelor’s degree and 8+ years of work experience, or an equivalent combination of education and work experience.
• 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.
• 5+ years of hands-on specialization in causal inference, experimental design, measurement, and optimization, such as A/B testing, quasi-experiments, observational causality, Marketing 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 using 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 detail regarding 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.
• Preferred qualification text also specifies 6+ years of relevant industry experience in Data Science, Applied Statistics, or ML engineering, and 6+ years with a PhD.

Responsibilities

• Drive the 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 causal impacts of promotional and non-promotional activities and generate actionable business optimization recommendations.
• Partner with product managers, data engineers, ML engineers, MLOps, and commercial stakeholders to translate business priorities into technical requirements and industrialize Causal AI applications.
• Serve as a Causal AI technical authority and subject matter expert; provide technical guidance, conduct code and design reviews, and mentor team members.
• Explain complex causal modeling and statistical concepts in clear narratives and actionable guidance for technical teams and business leaders.
• Monitor advancements in Causal AI, Causal ML, Bayesian modeling, and Generative AI; advocate for responsible AI 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 linked page; specific benefits are not listed in the posting.

What This Job Offers

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