Data Scientist (AI / Causal Inference) - Local - f2f

Nexiva • Cincinnati, OH

1d • Onsite

About The Position

We are seeking a Data Scientist with a strong background in Causal Inference and a willingness to learn AI technologies. This role involves designing and deploying AI solutions, applying causal inference techniques to measure business impact, and building scalable machine learning pipelines. You will collaborate with stakeholders to translate business problems into AI-driven solutions and stay abreast of emerging AI/ML technologies. The position is a 12-month contract-to-hire role located in Cincinnati, OH, requiring 5 days per week onsite.

Requirements

• 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
• 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.
• MLOps experience (CI/CD, model deployment, monitoring, workflow orchestration)
• Familiarity with experimentation frameworks and measurement pipelines
• Must be local to Cincinnati, OH
• Must have LinkedIn profile
• Must have local project experience

Nice To Haves

• AI experience is preferred; however, candidates with limited AI exposure are welcome if they have a strong willingness to learn and grow.
• Experience in Retail, CPG, Media, or Marketplace Analytics

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.
• Mentor and guide fellow data scientists.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed