Data Scientist Level 2

Kforce has a client that is seeking a Data Scientist Level 2 in Cincinnati, OH. 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
• Bachelor's or Master's in Statistics, Data Science, Computer Science, Applied Math, Economics, or related quantitative field
• 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

• 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