Data Scientist - Remote

About the Company

The Personalization and Loyalty team focuses on making the company a preferred destination for its customers. By delivering relevant offers and value at the right time, the team works to strengthen customer relationships and build long-term loyalty. Its approach combines statistical science, causal inference, and AI to design experiments, measure business impact, and develop insights that improve customer value.

About the Role

The team is looking for a Data Scientist to advance its capabilities in Generative AI, causal machine learning, and measurement science. This is a senior individual contributor position for someone with strong technical skills who can take data science solutions from research and experimentation through production deployment.

You will work closely with product managers and cross-functional stakeholders to shape the direction of science products and develop scalable solutions for personalization and loyalty. The role combines hands-on development, causal analysis, experimentation, and technical leadership, with opportunities to explore emerging AI methods and help build a more unified science framework.

Key Responsibilities

• Design, develop, and deploy Generative AI solutions, including LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG) pipelines, agentic workflows, and integration with existing measurement and science processes.

• Lead the full development lifecycle of data science solutions, from research and experimentation to production deployment, ensuring that systems are reliable, reproducible, and maintainable.

• Partner with product managers and cross-functional stakeholders to define product vision, establish roadmaps, and prioritize science capabilities in the personalization and loyalty space.

• Contribute to the development of a unified science layer that connects existing science capabilities into a scalable framework.

• Apply and extend causal machine learning and econometric methods, including conditional average treatment effects (CATE), difference-in-differences (DiD), matching, and panel methods.

• Develop and maintain production machine learning and experimentation pipelines using sound MLOps and software engineering practices, including CI/CD, version control, testing, and documentation.

• Evaluate emerging AI and machine learning technologies and identify practical opportunities to bring new approaches into production.

• Provide technical guidance and informal mentorship to colleagues, with the opportunity to take on formal mentoring responsibilities as junior team members join.

• Explain technical methods, analytical findings, and business implications clearly to technical teams, leadership, product managers, and other stakeholders.

• Measure the impact of different treatments on customer purchasing behavior and connect analytical findings to business outcomes.

Required Qualifications

• At least 3 years of applied data science experience, with demonstrated growth in responsibility and technical complexity.

• Hands-on experience with Generative AI applications, including at least one of the following: LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development.

• Familiarity with causal machine learning or causal inference methods, including treatment effect modeling, CATE, DiD, or 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 machine learning systems using software engineering best practices.

• Ability to translate business needs into data science solutions and collaborate with product managers on priorities and roadmaps.

• Strong written and verbal communication skills, including the ability to explain technical concepts to business audiences.

• Ability to work effectively in ambiguous, evolving problem spaces and contribute to early-stage technical direction and strategy.

• Understanding of econometrics, causal inference, and measurement processes, including evaluating how different treatments influence purchasing behavior.

Preferred Qualifications

• Experience with MLOps practices, including workflow orchestration, model monitoring, reproducibility, and deployment.

• Experience in retail, consumer packaged goods (CPG), media, or marketplace analytics.

• Experience mentoring or coaching colleagues on technical practices.

• Familiarity with experimentation frameworks and measurement pipelines.

Location and Work Arrangement

The primary location is Cincinnati, Ohio, with an onsite requirement of five days per week. Relocation is supported, but candidates must relocate within the first three months of employment. Candidates based in Chicago may be considered if suitable local candidates are unavailable, with occasional travel to Cincinnati required. Remote arrangements may also be considered for an exceptionally strong fit.