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About the Role

Serve as a technical leader and domain authority for advanced experimentation, measurement, and optimization solutions. Drive the design, development, and industrialization of enterprise-grade causal AI and statistical models to measure business impact, optimize resource allocation, and guide data-driven decision-making. Shape technical strategy for measurement and experimentation, mentor data scientists, and partner with cross-functional teams to deliver scalable AI solutions.

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.

Requirements

Bachelor’s Degree and 8+ years of work experience, or an equivalent mix of education and work experience.
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.
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.
6+ years of relevant industry experience in Data Science, Applied Statistics, or ML engineering 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.

Skills

Causal AI
Experimentation
Measurement
Optimization
Statistical Modeling
Econometric Modeling
Machine Learning
Causal Inference
A/B Testing
Quasi-Experimentation
Media Mix Modeling
Synthetic Controls
Uplift Modeling
Non-parametric Measurement
Python
R
SQL
PyTorch
TensorFlow
scikit-learn
statsmodels
CausalML
DoWhy
EconML
Robyn
LightweightMMM
Meridian
Spark
Databricks
Hadoop
AWS
GCP
Azure
Deep Learning
Generative AI
Bayesian Modeling

Location

South San Francisco

Work Type

Hybrid

Experience Level

Principal
8+ years
5+ years
6+ years

Education Level

Bachelor’s Degree
MS
PhD

Salary/Compensations

$217,400/yr to $403,700/yr

Benefits

Discretionary annual bonus
Relocation benefits are NOT offered

About the Company

Genentech is an equal opportunity employer.

Equal Opportunity

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.