Job Summary
We are seeking an experienced Data Scientist to drive causal inference, experimentation, measurement, personalization, and applied AI initiatives. The ideal candidate will have hands-on experience applying causal inference and econometric techniques to measure business impact, build production-ready machine learning solutions, and translate analytical insights into measurable business outcomes. Experience with Generative AI is a plus but not the primary requirement.
Key Responsibilities
Design and implement causal inference and causal machine learning solutions.
Measure the impact of business treatments on customer behavior, revenue, retention, and engagement.
Apply statistical methods including:
Difference-in-Differences
Matching
Panel Data Models
CATE Estimation
Uplift Modeling
Heterogeneous Treatment Effect Modeling
Define treatments, control groups, counterfactuals, outcome metrics, and evaluation windows.
Build scalable, production-ready ML pipelines using software engineering and MLOps best practices.
Partner with business and product teams to convert business problems into scientific solutions.
Develop and integrate Generative AI solutions including RAG, prompt engineering, LLM workflows, fine-tuning, and agentic AI where applicable.
Evaluate emerging AI/ML technologies for production adoption.
Present technical findings and business impact to both technical and non-technical stakeholders.
Provide technical guidance and code reviews to team members.