Senior Data Scientist, AI

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well‑being, and work‑life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them.

Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself.

*** Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT.***

The Senior Data Scientist of Responsible AI serves as a senior individual contributor within Empower's Responsible AI (RAI) team. This role embeds directly with multiple AI delivery teams as a matrixed partner, ensuring AI and GenAI systems are measurable, transparent, safe, and aligned with Responsible AI principles. The position focuses on evaluation methodology, model behavior analysis, and operationalizing Responsible AI practices in collaboration with engineering, QA, security, and governance partners.

• Lead evaluation and validation of AI and GenAI systems, including assessment of hallucination, fairness, robustness, explainability, and other model behavior risks.
• Design and implement repeatable evaluation workflows, benchmark datasets, and structured model behavior tests.
• Serve as the Responsible AI data science partner for assigned AI delivery teams, guiding the integration of RAI metrics, guardrails, and evaluation practices.
• Translate research findings and experimental methods into scalable, platform‑ready evaluation capabilities.
• Collaborate with AI/ML engineers and QA analysts to align evaluation logic with testing pipelines and deployment workflows.
• Support the creation of model cards, evaluation documentation, and transparency artifacts required for governance and review.
• Provide Responsible AI consultation to product, engineering, risk, legal, and governance stakeholders.
• Mentor junior and mid‑level data scientists on evaluation design, experimental rigor, and Responsible AI practices.

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field, or equivalent experience.
• 6+ years of professional data science experience.
• Demonstrated experience developing or evaluating AI/ML systems in production or pre‑production environments.
• Strong grounding in experimental design, statistical analysis, and model evaluation methodology.
• Experience collaborating with engineering and product teams in an agile, iterative delivery environment.
• Proficiency in Python and SQL for data analysis and evaluation workflows.
  Experience working with modern cloud platforms such as AWS.
• Strong communication skills and ability to operate effectively in a matrixed team model.

• Cloud platforms such as AWS, including managed data and ML services.
• Data storage and retrieval technologies such as relational and No
  
  SQL databases.
• Data analysis and visualization tools such as Python libraries, Quick Sight, Tableau, or Power BI.
• Collaboration and delivery tools supporting agile development and cross‑functional workflows.

• Contribute to Responsible AI and security‑by‑design practices by ensuring evaluation methods consider safety, misuse, and model risk.
• Partner with engineering, security, and governance teams to identify, document, and mitigate AI‑related risks surfaced through evaluation activities.
• Support evolving security and risk requirements as they relate to AI system behavior, testing, and governance reviews.

• Experience evaluating Generative AI or LLM‑based systems.
• Hands‑on work with explainability, fairness analysis, hallucination detection, or robustness testing techniques.
• Familiarity with Responsible AI principles, governance frameworks, or model documentation practices.
• Experience…