Data Scientist

Hudson Manpower•Cincinnati, OH

About The Position

We are seeking an experienced Data Scientist to drive causal inference, experimentation, measurement, personalization, and applied AI initiatives. The ideal candidate will have hands-on experience applying causal inference and econometric techniques to measure business impact, build production-ready machine learning solutions, and translate analytical insights into measurable business outcomes. Experience with Generative AI is a plus but not the primary requirement.

Requirements

3+ years of applied Data Science experience.
Strong experience with causal inference, causal ML, econometrics, or experimentation.
Experience measuring treatment effects and incremental business impact.
Hands-on experience with: Difference-in-Differences Matching CATE Panel Data Analysis Uplift Modeling Heterogeneous Treatment Effects
Strong Python and SQL programming skills.
Experience with Git.
Experience developing production-quality ML or analytics solutions.
Strong analytical, communication, and problem-solving skills.
Bachelor's or Master's degree in Statistics, Economics, Data Science, Computer Science, Applied Mathematics, or related quantitative field.

Nice To Haves

Experience with Generative AI, RAG, Prompt Engineering, Fine-tuning, LLM Evaluation, or Agentic AI.
Experience with Azure, Databricks, or similar cloud platforms.
Experience with MLOps, deployment, orchestration, monitoring, and model lifecycle management.
Experience building experimentation platforms or measurement pipelines.
Retail, CPG, media, personalization, loyalty, or customer analytics experience.
Experience mentoring technical teams.

Responsibilities

Design and implement causal inference and causal machine learning solutions.
Measure the impact of business treatments on customer behavior, revenue, retention, and engagement.
Apply statistical methods including: Difference-in-Differences Matching Panel Data Models CATE Estimation Uplift Modeling Heterogeneous Treatment Effect Modeling
Define treatments, control groups, counterfactuals, outcome metrics, and evaluation windows.
Build scalable, production-ready ML pipelines using software engineering and MLOps best practices.
Partner with business and product teams to convert business problems into scientific solutions.
Develop and integrate Generative AI solutions including RAG, prompt engineering, LLM workflows, fine-tuning, and agentic AI where applicable.
Evaluate emerging AI/ML technologies for production adoption.
Present technical findings and business impact to both technical and non-technical stakeholders.
Provide technical guidance and code reviews to team members.