Senior Data Scientist – Gen AI & Causal ML (Remote)

Strategic Data Systems, Inc.; Cincinnati (OH); Hybrid; USD 110,000 - 150,000; Part time

Benefits offered by this job

Medical, dental, and vision insurance

Life and disability insurance

401(k) with company match

Paid vacation time

Paid bench time

Training allowance

Referral bonuses

Job summary

SDS, Inc. is seeking a Data Scientist II to advance our Gen AI and causal ML capabilities, leading end-to-end development of scalable science solutions and partnering with product teams to drive strategy in personalization and loyalty.

The role emphasizes production-ready ML systems, strong collaboration across cross-functional teams, and mentorship of junior engineers in a fast-paced environment.

Qualifications

• 3+ years of applied data science experience, with progression in scope and technical complexity.
• Experience with Generative AI applications, including one or more of: LLM fine-tuning, prompt engineering, RAG pipelines, or agentic workflow development.
• Familiarity with causal ML and/or causal inference methods (e.g., CATE, heterogeneous treatment effect modeling, DiD, matching).
• Strong proficiency in Python, SQL, and Git.
• Experience with Azure and Databricks, or comparable cloud-based data science platforms.
• Experience contributing 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, with the ability to translate between technical and business audiences.
• Comfort with ambiguity—able to operate effectively in evolving problem spaces and contribute to early-stage vision and strategy.
• Bachelor's or Master's in Statistics, Data Science, Computer Science, Applied Math, Economics, or related quantitative field.

Responsibilities

• Advance our 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 the personalization and loyalty space.
• Contribute to the vision and early development of a holistic science layer—working to connect and consolidate scattered science capabilities into a unified, scalable framework.
• Apply and extend causal ML and econometric methods (e.g., CATE, DiD, matching, 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 on the team, 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.

Skills

Generative AI

Causal ML

Python

SQL

Git

Azure

Databricks

MLOps

Production ML

Education

Bachelor's degree in Statistics/Data Science/CS/Applied Math/Economics

Master's degree in Statistics/Data Science/CS/Applied Math/Economics

Tools

Azure

Databricks

CI/CD

Git workflows

Job description

SDS, Inc. is seeking a Data Scientist II to advance our Gen AI and causal ML capabilities, leading end-to-end development of scalable science solutions and partnering with product teams to drive strategy in personalization and loyalty.

The role emphasizes production-ready ML systems, strong collaboration across cross-functional teams, and mentorship of junior engineers in a fast-paced environment.