Job Summary: We are seeking a highly skilled Data Scientist with expertise in Causal Inference, Econometrics, and Machine Learning to develop scalable AI-driven solutions that enhance customer personalization and loyalty. The ideal candidate will have experience building production-ready ML models, applying statistical methodologies to solve business problems, and leveraging Generative AI technologies such as LLMs, RAG, and Prompt Engineering. This is a senior individual contributor role offering the opportunity to influence AI strategy and deliver measurable business impact.

Project Details:

Design and deploy Generative AI solutions using LLMs, Prompt Engineering, RAG, and Agentic AI workflows.
Apply Causal Inference and Econometric techniques to measure business outcomes and customer behavior.
Develop scalable ML models and experimentation pipelines.
Collaborate with Product Managers and cross-functional teams to build AI-powered science solutions.
Work with Azure, Databricks, Python, SQL, and Git to develop production-ready applications.
Must Haves
3+ years of Data Science experience.
Strong experience with Causal Inference and Econometrics.
Experience quantifying treatment effects and translating analytical results into business impact.
Proficiency in Python, SQL, and Git.
Experience with Azure, Databricks, or similar cloud-based platforms.
Hands-on experience with Generative AI, including one or more of:
LLM Fine-Tuning
Prompt Engineering
Retrieval-Augmented Generation (RAG)
Agentic AI Workflows
Strong communication skills and the ability to work with technical and business stakeholders.
Desired Skills
Experience with Causal ML techniques such as CATE, Difference-in-Differences (DiD), Matching, and Heterogeneous Treatment Effect Modeling.
Experience with MLOps, CI/CD, model deployment, and monitoring.
Background in Retail, CPG, Media, or Marketplace Analytics.
Experience with experimentation frameworks and measurement pipelines.
Ability to mentor junior team members and contribute to technical leadership.