QUALIFICATIONS, SKILLS & EXPERIENCE

3+ years of applied data science experience, with demonstrated progression in scope and technical complexity
Hands-on experience with Generative AI applications, including one or more of: LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development
Familiarity with causal ML and/or causal inference methods (e.g., CATE, heterogeneous treatment effect modeling, DiD, matching)
Strong proficiency in Python, SQL, and Git
Experience with Azure and Databricks, or comparable cloud-based data science platforms
Experience contributing to production-quality ML systems using software engineering best practices
Ability to partner with product managers and stakeholders to translate business needs into science solutions and roadmap priorities
Strong oral and written communication skills, with the ability to translate between technical and business audiences
Comfort with ambiguity—able to operate effectively in evolving problem spaces and contribute to early-stage vision and strategy
Bachelor's or Master's in Statistics, Data Science, Computer Science, Applied Math, Economics, or related quantitative field

Preferred:

Experience with MLOps practices including workflow orchestration, model monitoring, reproducibility, and deployment
Experience in retail, CPG, media, or marketplace analytics
Demonstrated ability to informally mentor or coach peers in technical best practices
Familiarity with experimentation frameworks and measurement pipelines

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