Empower

Senior Data Scientist, Responsible AI

Empower
US Kansas City, MO
Onsite 2026-06-29
Estimated salary · Kansas City
~ $99,400 - $169,800
Low
$75K
Median
$99K
High
$131K
Market in Kansas City · BLS OEWS 2025

Job description

<p>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.</p> <p>Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself.</p> <p>***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.***</p> <p></p> <p>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.</p> <h2></h2> <h2>What you will do:</h2> <ul><li>Lead evaluation and validation of AI and GenAI systems, including assessment of hallucination, fairness, robustness, explainability, and other model behavior risks.</li><li>Design and implement repeatable evaluation workflows, benchmark datasets, and structured model behavior tests.</li><li>Serve as the Responsible AI data science partner for assigned AI delivery teams, guiding the integration of RAI metrics, guardrails, and evaluation practices.</li><li>Translate research findings and experimental methods into scalable, platform-ready evaluation capabilities.<

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