Candidates should be eligible to work for any employer in the United States without needing Visa sponsorship now or in the future
We're seeking a Data Scientist to help shape the future of our AI and science capabilities. This is a senior individual contributor role for a technically strong, forward-thinking data scientist who can advance our Gen AI and causal ML capabilities, lead end-to-end development of scalable science solutions, and partner with product and cross-functional teams to drive vision and strategy in our space.
Key Responsibilities
- Advance our AI capabilities by designing, developing, and deploying Gen AI solutions—including LLM fine-tuning, prompt engineering, RAG pipelines, agentic workflows, and integration of Gen AI into existing measurement and science workflows.
- Lead end-to-end development and scaling of data science solutions, from research and experimentation through production, ensuring solutions are robust, reproducible, and maintainable.
- Partner with product managers and cross-functional stakeholders to shape the vision, roadmap, and prioritization of science products and capabilities in the personalization and loyalty space.
- Contribute to the vision and early development of a holistic science layer—working to connect and consolidate scattered science capabilities into a unified, scalable framework.
- Apply and extend causal ML and econometric methods (e.g., CATE, DiD, matching, panel methods) to support measurement, experimentation, and personalization at scale.
- Build, maintain, and improve production ML and experimentation pipelines using sound MLOps and software engineering practices, including CI/CD, version control, testing, and documentation.
- Research and evaluate emerging AI/ML technologies and methodologies, identifying opportunities to bring state-of-the-art approaches into production.
- Serve as a technical leader and subject matter expert on the team, providing guidance and informal mentorship to peers and evolving into a formal mentor as junior talent joins the team.
- Communicate complex technical findings and methodologies clearly to both technical and non-technical audiences, including leadership and product stakeholders.
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