"Data Scientist - Remote "

ABOUT THE COMPANY
The Personalization and Loyalty team focuses on making the company a preferred destination for customers. Its work centers on delivering relevant value at the right time, strengthening customer relationships, and building loyalty through data-driven insights. The team uses statistical science, causal inference, and AI to design experiments, measure business impact, and improve customer outcomes.
ABOUT THE ROLE
We are looking for a Data Scientist to help advance the team's Generative AI and causal machine learning capabilities. This is a senior individual contributor position for someone who can develop scalable data science solutions, take projects from research through production, and work closely with product managers and cross-functional teams to shape the direction of science capabilities in personalization and loyalty.
The role combines applied data science, causal inference, econometrics, and AI development. You will help improve measurement processes, understand how different treatments influence customer purchasing behavior, and build reliable machine learning solutions that support business decisions. Strong technical skills are important, along with the ability to work through ambiguity and communicate findings clearly.
LOCATION AND WORK ARRANGEMENT
The preferred location is Cincinnati, Ohio, with onsite work 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 local candidates are unavailable and must be able to travel to Cincinnati occasionally. Remote candidates may also be considered if they are an exceptional fit.
KEY RESPONSIBILITIES
Design, develop, and deploy Generative AI solutions, including LLM fine-tuning, prompt engineering, RAG pipelines, and agentic workflows, and integrate them into existing science and measurement processes.
Lead data science projects from research and experimentation through production, ensuring solutions are scalable, reliable, reproducible, and maintainable.
Work with product managers and cross-functional stakeholders to define science product priorities, roadmaps, and long-term direction for personalization and loyalty.
Help develop a unified science framework by connecting existing capabilities into a more consistent and scalable approach.
Apply causal machine learning and econometric methods, including CATE, difference-in-differences (DiD), matching, and panel methods, to support experimentation, measurement, and personalization.
Build and maintain production machine learning and experimentation pipelines using CI/CD, version control, testing, documentation, and other software engineering best practices.
Evaluate emerging AI and machine learning technologies and identify approaches that can be applied effectively in production.
Share technical knowledge with colleagues, provide informal mentorship, and contribute technical guidance as the team grows.
Explain technical findings and methodologies to both technical and non-technical audiences, including business stakeholders and leadership.
REQUIRED QUALIFICATIONS
At least three years of applied data science experience, with increasing scope and technical complexity.
Hands-on experience with Generative AI applications, including at least one of LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development. Candidates should be willing to continue learning and developing their AI skills.
Familiarity with causal machine learning or causal inference methods, such as CATE, heterogeneous treatment effect modeling, difference-in-differences, 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 sound software engineering practices.
Understanding of causal inference, econometrics, and measurement processes, including how different treatments affect business outcomes and customer purchasing behavior.
Ability to work with product managers and stakeholders to translate business needs into science solutions and roadmap priorities.
Strong written and verbal communication skills, with the ability to explain technical concepts to business and technical audiences.
Comfort working in ambiguous, evolving problem spaces and contributing to early-stage planning and strategy.
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 best practices.
Familiarity with experimentation frameworks and measurement pipelines.

Work arrangement
Yes