Data Scientist (AI / Causal Inference) Contract-to-Hire- F2F interview

Data Scientist (AI / Causal Inference) Contract-to-Hire

Duration

• 12-Month Contract-to-Hire

Work Authorization

Location

• Cincinnati, OH 5 Days/Week Onsite
• Chicago, IL candidates may be considered if highly qualified, but must be willing to travel to Cincinnati as needed.

Interview Process

1. Hiring Manager ONSITE
2. Technical Interview with the Data Science Team

Must-Have (Non-Negotiable) Skills

• Strong experience with Causal Inference
• Experience with Econometrics
• Expertise in measurement frameworks/processes
• Ability to quantify treatment impact and connect analytical outcomes to business performance (e.g., measuring changes in customer purchasing behavior based on different treatments)
• AI experience is preferred; however, candidates with limited AI exposure are welcome if they have a strong willingness to learn and grow.

Technical Requirements

• 3+ years of hands-on Data Science experience
• Strong proficiency in Python, SQL, and Git
• Experience with Azure, Databricks, or similar cloud platforms
• Knowledge of Generative AI, including one or more of:
  • LLM Fine-Tuning
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI Workflows
• Experience with Causal Machine Learning techniques such as:
  • CATE
  • Difference-in-Differences (DiD)
  • Matching
  • Heterogeneous Treatment Effect Modeling
• Experience building and deploying production-ready ML solutions using software engineering best practices.

Preferred Qualifications

• MLOps experience (CI/CD, model deployment, monitoring, workflow orchestration)
• Experience in Retail, CPG, Media, or Marketplace Analytics
• Familiarity with experimentation frameworks and measurement pipelines
• Ability to mentor and guide fellow data scientists

Key Responsibilities

• Design and deploy Generative AI solutions using LLMs, RAG, prompt engineering, and agentic workflows.
• Apply causal inference and econometric techniques to measure business impact and improve personalization.
• Build scalable machine learning and experimentation pipelines.
• Partner with Product Managers and business stakeholders to translate business problems into AI-driven solutions.
• Research and implement emerging AI/ML technologies.
• Communicate technical findings effectively to both technical and business audiences.

Ayush Sharma Sr. US Technical Recruiter

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