Senior Data Scientist (Hiring Immediately)

About Warden AI

AI is being deployed across every industry, transforming how decisions are made and how people interact with technology. But as adoption accelerates, so do concerns about bias, accuracy, and accountability. Warden AI safeguards this transformation by making sure AI systems are fair, transparent, accurate, and explainable.

Founded in 2023 and backed by investors from Playfair, Monzo, Onfido, and Codat, our platform continuously audits AI models, delivering independent oversight through dashboards, reports, and certifications. With teams in London and Austin, we partner with both fast-growing platforms and global enterprises to enable the responsible adoption of AI worldwide.

Read why Playfair Capital invested in Warden AI .

About the role

We are hiring a Senior Data Scientist to define the analytical standards that underpin our evaluation of high-stakes AI systems. The role spans fairness evaluation, rigorous statistical analysis, and an applied understanding of hiring and selection procedures. Most candidates will start strongest in one of these areas and develop depth across all three, enabling you to influence everything from how we design tests and interpret results to how we guide customers, shape product decisions, and meet the expectations of an evolving responsible AI landscape.

You will report to the CTO and work closely with the founders and product team across hands-on analysis, methodological design, and strategic thinking. Your work will elevate our analytical standards, strengthen the confidence customers place in us, and play a central role in establishing Warden as the standard-setter for rigorous, defensible evaluations.

As one of our early data hires, you will have high agency to shape both how our analytical function evolves and the scope of your own role as we grow.

What you’ll do

Here are a few examples of things you might be working on:

• Set and uphold rigorous analytical methodology. Define the statistical tests, fairness metrics, sampling strategies, and evaluation frameworks we rely on, and embed the checks and validation patterns that keep our analytical work accurate, reproducible, and defensible.
• Translate regulations and standards into practical tests. Turn legal requirements, guidance, and emerging HR and AI standards into clear, defensible audit procedures and criteria.
• Design the foundations for audit execution. Create the datasets, test frameworks, workflows, and analysis patterns that enable consistent, efficient, and high-quality audits.
• Take a long-term, strategic view. Identify emerging risks, opportunities, regulatory shifts, and industry developments, and help define how our AI assurance approach should evolve over the next 12–24 months.
• Guide the evolution of our long-term data capabilities. Anticipate the data assets and analytical foundations we will need as our product expands and the regulatory landscape evolves.
• Define how we analyze and interpret results. Establish the principles, evidence thresholds, and approaches for handling uncertainty and limitations, and help the team communicate findings clearly and consistently.
• Support key high-stakes conversations. Bring technical authority on data, methodology, and context to stakeholder discussions and help address detailed questions with confidence.
• Contribute to documentation and external credibility. Write accessible explanations of our approach and contribute to white papers or blog posts to help build trust in our work.

What you should bring

• Relevant academic or equivalent background with a strong, professional senior-level track record over 5+ years and deep expertise in at least two of the following areas:
• AI bias and responsible AI, including fairness evaluation, model assessment, or the design of responsible-AI practices in applied settings.
• HR analytics or I-O psychology, with experience in selection processes, adverse impact analysis, validity considerations, or defensible evaluation practices.
• Statistically rigorous analytical work in regulated or high-stakes environments, with fluency in statistical reasoning, demonstrated through defensible, reproducible analysis.
• Fluency in Python for analytical work. You’re comfortable using Python for statistical analysis, data preparation, and reproducible evaluation workflows.
• Grow expertise across domains. You take ownership of your development and quickly build expert-level competence]]>