Principal Data Scientist
Core
Technical leader designing, building, and industrializing enterprise-grade causal AI and statistical models to measure business impact and optimize resource allocation.
Role type
Principal Data Scientist (Causal AI & Experimentation)
Builds
Production-grade statistical, econometric, machine learning, and causal inference models (A/B testing, quasi-experiments, MMM, synthetic controls, uplift modeling)
Domain
Healthcare/Pharmaceuticals (preferred) + Causal AI / Experimental Design / Measurement
Deliverable
production ML models
Required skills
Causal inference, Experimental design, Statistical modeling, Python, R, SQL, PyTorch, TensorFlow, scikit-learn, CausalML, DoWhy, EconML, Spark, Databricks, AWS/GCP/Azure
Preferred skills
Deep Learning, Generative AI, Healthcare data structures, Open-source contributions, Peer-reviewed publications
Technologies
PyTorch, TensorFlow, scikit-learn, statsmodels, CausalML, DoWhy, EconML, Robyn, LightweightMMM, Meridian, Spark, Databricks, Hadoop, AWS, GCP, Azure
Responsibilities
Drive technical design and methodology for Causal AI projects; Design, build, and deploy production-grade causal models; Develop scalable algorithms to quantify causal impact; Partner with cross-functional teams to industrialize solutions; Act as SME providing technical guidance and code reviews; Translate complex modeling concepts into actionable business narratives
Seniority
Principal, hands-on IC with mentorship
