Senior Data Scientist - 26-00755

LeadStack Inc.; Cincinnati (OH); On-site; USD 110,000 - 180,000; Full time

Job summary

LeadStack Inc. is seeking a senior Data Scientist to shape the future of AI and analytics capabilities in a fast‑growing environment.

You will lead end‑to‑end data science initiatives, including Gen AI, causal ML, and scalable measurement across personalization and loyalty domains. The role requires collaboration with product and cross‑functional teams, driving vision, roadmap, and techn stack decisions while mentoring peers.

Qualifications

• 3+ years of applied data science experience with increasing scope.
• Hands‑on Generative AI experience: LLM fine‑tuning, prompts, RAG, or agentic workflows.
• Familiar with causal ML methods (CATE, DiD, matching).
• Strong Python, SQL, and Git skills.
• Experience with Azure and Databricks or comparable platforms.
• Experience shipping production ML systems with software engineering practices.
• Ability to translate business needs into science roadmaps.
• Excellent written and verbal communication; comfortable with ambiguity.

Responsibilities

• Advance Gen AI capabilities: design, develop, and deploy Gen AI solutions and integrations.
• Lead end‑to‑end data science development from research to production.
• Partner with product managers to shape vision, roadmap, and priorities.
• Contribute to a holistic, scalable science framework across capabilities.
• Apply causal ML and econometrics to measurement and personalization at scale.
• Build and maintain production ML and experimentation pipelines with MLOps.
• Evaluate new AI/ML approaches for production readiness.
• Provide mentorship and know‑how to peers and junior team members.
• Communicate complex results clearly to technical and business stakeholders.

Skills

Data science

Generative AI

LLM fine‑tuning

Prompt engineering

RAG pipelines

Causal ML

Python

SQL

Git

Azure

Databricks

MLOps

Production ML

Stakeholder collaboration

Communication

Ambiguity tolerance

Education

Bachelor’s or Master’s in Statistics/Data Science/CS/Applied Math/Economics

Tools

Azure

Databricks

Job description

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.

Job Title: Data Scientist

Duration: 12+ Months

Location: Cincinnati, OH or Chicago, IL

Only w2

Job Description

• AI – Not a dealbreaker if they do not have a ton of experience, but must be willing to learn
• 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
• 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.