Data Scientist

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.

Requirements

Required Qualifications; 3+ years of applied Data Science experience.; Strong experience with causal inference, causal ML, econometrics, or experimentation.; Experience measuring treatment effects and incremental business impact.; Hands-on experience with:; Difference-in-Differences; Matching; CATE; Panel Data Analysis; Uplift Modeling; Heterogeneous Treatment Effects; Strong Python and SQL programming skills.; Experience with Git.; Experience developing production-quality ML or analytics solutions.; Strong analytical, communication, and problem-solving skills.; Bachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, Applied Mathematics, or related quantitative field.; Preferred Qualifications; Experience with Generative AI, RAG, Prompt Engineering, Fine-tuning, LLM Evaluation, or Agentic AI.; Experience with Azure, Databricks, or similar cloud platforms.; Experience with MLOps, deployment, orchestration, monitoring, and model lifecycle management.; Experience building experimentation platforms or measurement pipelines.; Retail, CPG, media, personalization, loyalty, or customer analytics experience.; Experience mentoring technical teams.