Principal Data Scientist (Causal AI & Experimentation)

Location: South San Francisco, California, United States; hybrid schedule with 3 days per week on campus. Relocation benefits are not offered.

Salary: $217,400–$403,700 per year, plus a discretionary annual bonus may be available based on individual and Company performance.

Company

hirify.global develops medicines for serious and life-threatening diseases and invests in pharmaceutical research and development.

What you will do

• 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 causal impact and generate recommendations for business optimization.
• Partner with product managers, data engineers, ML engineers, MLOps, and commercial stakeholders to industrialize Causal AI applications.
• Act as a Causal AI subject matter expert by providing technical guidance, conducting code and design reviews, and mentoring data scientists.
• Explain complex causal modeling and statistical concepts to technical teams and business leaders, while bringing advances in Causal AI, Causal ML, Bayesian modeling, and Generative AI into the measurement ecosystem.

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, including 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, and EconML.
• Advanced proficiency in Python and/or R, plus strong SQL skills for complex database management and data manipulation.
• Deep familiarity with open-source measurement packages such as Robyn, LightweightMMM, and Meridian.
• Hands-on experience scaling algorithms with big data environments including Spark, Databricks, or Hadoop, and cloud platforms including AWS, GCP, or Azure.
• Exceptional verbal and written communication skills, with the ability to influence partners, align business priorities, and explain mathematical methodologies to business leaders.
• Strong critical thinking and rigorous attention to experimental design, bias mitigation, and causal validity.

Nice to have

• Advanced degree, preferably an MS or PhD, in Statistics, Mathematics, Applied Economics, Computer Science, Data Science, or a related quantitative field focused on AI/ML or Causal Inference.
• For candidates with a PhD, 6+ years of relevant industry experience in Data Science, Applied Statistics, or ML Engineering.
• Experience 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 with Causal AI frameworks.
• Contributions to open-source data science tools or peer-reviewed publications in causal inference, experimentation, or measurement technology.

Culture & Benefits

• Regular full-time employment with a hybrid campus-based work arrangement.
• Benefits are provided for the position.
• Equal employment opportunity and accommodation support are available for applicants with disabilities.
• The organization has invested $14.7 billion in R&D and has more than 40 FDA-approved medicines.