What are the responsibilities and job description for the Data Scientist (AI / Causal Inference) Contract-to-Hire- F2F interview position at Stellent IT LLC?
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
- Hiring Manager ONSITE
- 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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