Principal Data Scientist

What you'll do

• Design and develop production-grade statistical, econometric, and causal inference models including A/B testing and Marketing Mix Modeling.
• Translate complex business objectives into robust mathematical frameworks and scalable technical requirements for Causal AI applications.
• Build scalable algorithms to quantify the causal impact of promotional activities and generate business optimization recommendations.
• Lead the end-to-end industrialization of Causal AI solutions by partnering with data engineers, MLOps, and commercial stakeholders.
• Serve as a technical authority by performing code reviews, providing design guidance, and mentoring senior and junior data scientists.
• Communicate complex causal modeling concepts into clear, actionable narratives for both technical teams and non-technical business leaders.
• Research and integrate academic advancements in Causal ML, Bayesian modeling, and Generative AI into the measurement ecosystem.

What we're looking for

• A Bachelor’s Degree and 8+ years of work experience, or an equivalent mix of education and work experience, is required.
• 8+ years of relevant industry experience in Data Science, Applied Statistics, or ML engineering is required.
• 5+ years of hands-on specialization in Causal Inference, Experimental Design, Measurement, and Optimization is required.
• Expert-level knowledge of open-source frameworks for ML, statistics, and causal inference is required.
• Advanced proficiency in Python and/or R, along with strong SQL skills, is required.
• Hands-on experience scaling algorithms using big data environments and cloud platforms is required.
• An advanced degree (MS or PhD) in a quantitative field with 6+ years of relevant experience is preferred.
• Experience with large-scale datasets in healthcare, pharmaceuticals, or regulated environments is preferred.