Data Scientist

About the role

The Personalization and Loyalty team's mission is to make company the destination of choice for our customers. We drive loyalty by delivering what our customers want—at the right time, with the right value—creating meaningful connections that deepen their relationship with our company. As part of this organization, the team applies statistical science, causal inference, and AI to design experiments, measure impact, and scale insights that drive customer value and loyalty.

Location: Cincinnati – onsite 5 days a week. Open to relocation (must be within first 3 months of employment). May consider Chicago if no local candidates are found, with occasional travel to Cincinnati. Open to remote for a perfect fit.

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 productionization, 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

3+ years of applied data science experience, with demonstrated progression in scope and technical complexity

Skills

Required Skills:

• 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
• Causal Inferences Experience
• AI – Not a dealbreaker if they do not have a ton of experience, but must be willing to learn
• Econ Metrics
• Measurement processes
• Quantify treatments back to business (How does purchasing behavior change with different treatments)

Preferred Skills:

• 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