DTS is looking for experienced Data Scientist for our client position based in Chicago, IL/ Cincinnati, OH / Remote
The data scientist role is now available for fully remote options.
Top skills 1. Causal Inferences Experience 2. AI – Not a dealbreaker if they do not have a ton of experience, but must be willing to learn 3. Econ Metrics 4. Measurement processes 5. 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
COMPANY OVERVIEW
The Company knows customers, and we know how to connect you.
Using a sophisticated, proprietary suite of tools and technology, we turn customer data into actionable knowledge. With unparalleled customer data and predictive analytics capabilities, we deliver personalized marketing strategies and ensure the best experience for customers of the parent Company and more than 300 consumer-packaged-goods companies. We put the customer at the center of everything we do, resulting in a more dynamic, informed and personal approach to driving customer loyalty.
Job Description SUMMARY
The Personalization and Loyalty team's mission is to make the parent Company the destination of choice for our customers. We drive loyalty by delivering what our customers want—at the right time, with the right value—creating meaningful connections that deepen their relationship with the parent Company.
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 are 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 contributeto 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.
DTS offers an excellent compensation package.
Contact:
Kuldeep Singh
Team Lead
Digital Technology Solutions
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