GenAI Data Scientist

Eliassen Group; Blue Ash (OH); On-site; Confidential; Full time

Job summary

Eliassen Group is seeking a Data Scientist on site in Blue Ash, OH or Chicago, IL to advance Gen AI and causal ML capabilities. You will lead end-to-end development of scalable science solutions, partnering with product and cross-functional teams to drive personalization and loyalty initiatives.

The role requires production-ready ML delivery, strong communication, and mentorship as the team grows. On-site work in Blue Ash or Chicago with a competitive W2 package is offered.

Qualifications

• 3+ years of applied data science experience.
• Hands-on experience with Generative AI applications such as LLM fine-tuning and prompt engineering.
• Familiarity with causal ML and causal inference methods including CATE, DiD, and matching.
• Proficiency in Python, SQL, and Git.
• Experience with Azure and Databricks or comparable cloud-based data science platforms.
• Contributions to production-quality ML systems using software engineering best practices.
• Ability to translate business needs into science solutions and roadmap priorities.
• Strong oral and written communication skills for technical and non-technical audiences.

Responsibilities

• Advance Gen AI capabilities by designing, developing, and deploying Gen AI solutions including LLM fine-tuning, prompt engineering, RAG pipelines, agentic workflows, and integration into measurement workflows.
• Lead end-to-end development and scaling of data science solutions from research through productionization.
• Partner with product managers and cross-functional stakeholders to shape vision and prioritization of science products in personalization and loyalty.
• Contribute to a holistic science layer to connect scattered capabilities into a unified framework.
• Apply causal ML and econometric methods to support measurement, experimentation, and personalization at scale.
• Build and maintain production ML and experimentation pipelines with CI/CD, version control, testing, and documentation.
• Research emerging AI/ML technologies and bring state-of-the-art approaches into production.
• Serve as a technical leader and mentor to peers, evolving into formal mentorship as needed.
• Communicate complex findings clearly to technical and non-technical leadership.

Skills

Gen AI

causal ML

Python

SQL

Git

Azure

Databricks

MLOps

Communication

Tools

Azure

Databricks

Job description

Description:

On-site in either Blue Ash, OH or Chicago, IL

Our client seeks a Data Scientist to advance 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 personalization and loyalty. The role focuses on statistical science, causal inference, AI-driven experimentation, and the integration of insights that deliver customer value and loyalty.

We can facilitate w2 and corp-to-corp consultants. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $60.00 to $70.00/hr. w2

Responsibilities:

• Advance 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 personalization and loyalty.
• Contribute to the vision and early development of a holistic science layer to connect and consolidate scattered science capabilities into a unified, scalable framework.
• Apply and extend causal ML and econometric methods such as CATE, DiD, matching, and 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, 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.

Experience Requirements:

• 3+ years of applied data science experience with progression in scope and technical complexity.
• Hands-on experience with Generative AI applications such as LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development.
• Familiarity with causal ML and causal inference methods including CATE, heterogeneous treatment effect modeling, DiD, and matching.
• Proficiency in Python, SQL, and Git.
• Experience with Azure and Databricks or comparable cloud-based data science platforms.
• Contributions 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 to translate between technical and business audiences.
• Comfort with ambiguity and ability to operate effectively in evolving problem spaces and contribute to early-stage vision and strategy.
• Preferred: Experience with MLOps practices including workflow orchestration, model monitoring, reproducibility, and deployment.
• Preferred: Experience in retail, CPG, media, or marketplace analytics.
• Preferred: Ability to informally mentor or coach peers in technical best practices.
• Preferred: Familiarity with experimentation frameworks and measurement pipelines.