Data Scientist Level 2

Data Scientist

LeadStack Inc. is an award-winning, one of the nation's fastest-growing, certified minority-owned (MBE) staffing services provider of contingent workforce. As a recognized industry leader in contingent workforce solutions and Certified as a Great Place to Work, we're proud to partner with some of the most admired Fortune 500 brands in the world.

Top skills:

• 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)

Work location:

• Cincinnati - Onsite 5 days a week
• Open to relocation but must be within first 3 months of employment
• Could consider Chicago if no local candidates can be found, but they would need to travel on occasion to Cincinnati

Interview process details:

• Initial Screening with HM and Second round technical screening with member of the team

Prescreening Details:

• Standard for Now, but could switch to custom

SUMMARY: As part of this organization, the KM+ DSR team applies statistical science, causal inference, and AI to design experiments, measure impact, and scale insights that drive customer value and loyalty. 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.

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

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 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.