Responsible AI Senior Data Scientist

Workday Workday · Enterprise · Dublin, Ireland

Workday is seeking a Responsible AI Senior Data Scientist to join their Compliance & Integrity Team. This role focuses on developing analytic capabilities to implement policies and guidelines for responsible AI development. The scientist will contribute to evaluation capabilities, create customer-facing reports, and stay updated on emerging responsible AI practices like fairness, explainability, robustness, and observability. The role involves collaboration with ML engineers, product teams, and legal/privacy teams to guide ethical AI development.

What you'd actually do

  1. Partner with functions adjacent to Responsible AI, including product, legal, privacy, and security stakeholders to interpret principles, policies, and standards, and turn them into concrete, testable requirements and evaluation plans across the AI development lifecycle.
  2. Develop, maintain, and embed reusable evaluation methods and pipelines (e.g., for fairness, robustness, safety, or quality) into AI platforms and workflows so that product and engineering teams can easily test, monitor, and improve AI systems aligned to Workday’s Responsible AI governance framework
  3. Define, implement, and document a curated set of metrics, evaluation templates, and best practices that teams can consistently adopt across diverse AI use cases, supporting scalable and repeatable Responsible AI governance.
  4. Stay informed about developments in Responsible AI and AI evaluation, and translate relevant advances into practical recommendations, prototypes, and improvements to the team’s evaluation capabilities, with a strong focus on real-world impact.
  5. Produce clear, stakeholder-facing analyses and narratives that explain evaluation approaches, findings, and tradeoffs, and act as an advocate for integrating Responsible AI considerations early and consistently in product and model development.

Skills

Required

  • 5+ years experience working in a data science, data analytics or data engineering role or function.
  • Experience in a data science, data analytics, or data engineering role or function, applying statistical and/or machine learning methods to real-world data.
  • Proficiency in at least one programming language commonly used for data and ML (e.g., Python, R, or Java), demonstrated through production systems, open-source contributions, or substantial projects.
  • Working knowledge of statistical analysis and hypothesis testing, including designing experiments and interpreting results.
  • Hands-on experience with modern ML and/or LLM tooling and platforms (e.g., Kubeflow, SageMaker, Databricks, or comparable tools), and familiarity with LLM observability and evaluation frameworks (e.g., LangSmith or similar).
  • Demonstrated experience incorporating responsible AI practices into your work (e.g., fairness, transparency, ethics, and privacy considerations in AI systems).

What the JD emphasized

  • Responsible AI
  • evaluation methods
  • evaluation capabilities
  • AI evaluation

Other signals

  • responsible AI
  • AI governance
  • evaluation capabilities
  • fairness
  • explainability
  • robustness
  • observability