Senior Applied Scientist / Data Scientist

About Warden AI

Warden AI helps HR Tech vendors, staffing firms, and employers prove and improve the fairness, accuracy, and compliance of their AI hiring systems. In a fast-changing regulatory environment, organizations need a trusted partner to validate their AI, and Warden is quickly becoming the default solution.

In 2025, Warden grew from 4 to 40 customers. In 2026, we’ve already more than doubled ARR and are growing faster every quarter. That growth is driven not just by market demand, but by something rarer: a genuine network effect. Customers want to promote working with Warden because it helps them win their own deals.

About the role

We're hiring a Senior Applied Scientist / Data Scientist to run highly defensible bias audits of high-stakes AI systems, used everywhere from startups to industry-leading enterprises. These audits carry real weight with regulators, courts, customers and candidates, and the methodology behind them has to hold up. The role spans AI system evaluation, rigorous statistical analysis, synthetic data generation, and an applied understanding of hiring and selection procedures.

You'll join a team with deep expertise in behavioral and social sciences, evaluation methodology, and structured bias testing. You'll need strong statistical and probabilistic reasoning: fluency with techniques like uncertainty quantification, experimental design, causal inference and Bayesian modeling. Just as essential is commercial instinct: the judgment to design rigorous, workable methods within real product goals and constraints, built through meaningful, hands-on experience shipping at startup speed.

You will report to the CTO and work closely with the founders and product team across hands-on analysis, methodological design, and strategic thinking. As one of a small number of 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 statistical 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. Work with our policy experts to translate legal requirements, guidance, and emerging HR and AI standards into clear, practical audit methodologies.
• Design the foundations for audit execution. Create the datasets, test frameworks, workflows, and analysis patterns that enable consistent, efficient, and high-quality audits, and help establish the data partnerships that inform our synthetic data generation and audit methodology.
• Analyze and interpret audit results. Assess what results do and don't show given uncertainty and limitations, and shape how findings are presented to customers through our reports and dashboards, including actionable insight like pointing to the likely source when an audit fails.
• Support key high-stakes conversations. Bring technical authority on data, methodology, and statistical defensibility to stakeholder discussions, including with customers’ own data science, legal and compliance teams.
• Document and defend our methodology. Write the accessible explanations, white papers and industry reports that let regulators, customers and outside experts scrutinize our approach and trust the results.

What you should bring

• 6+ years in a commercial, high-performing product organization doing applied statistics and data science - not solely academic or research-lab experience.
• Strong statistical and probabilistic reasoning , with hands-on fluency in confidence intervals, hypothesis testing, power analysis, experimental design, causal inference, and Bayesian modeling.
• Experience designing evaluation methodologies - including black-box and counterfactual approaches - and the test datasets behind them, with good judgment on realism and validity limits.
• Practical familiarity with evaluating AI systems and their failure modes, across classic ML models, LLM-based systems, and AI agents.
• Fluency in Python for statistical analysis, data preparation, and reproducible evaluation workflows.
• A rigorous approach that values evidence, clarity, and defensibility, producing analysis that stands up to scrutiny.
• Comfort with open-ended problems and the ability to bring structure where none exists.
• Clear, responsible communication, with the ability to adapt complex ideas for different audiences, and a collaborative, high-agency way of working with colleagues across the company and with customers.

Nice to have, not required:

• Background in HR analytics / I/O psychology / Psychometrics (selection processes, adverse impact analysis, etc)
• Familiarity with AI and hiring regulation, especially translating its requirements into testable criteria.
• First-hand experience with how modern agentic AI systems are built and their behavior emerges
• Experience with explainability for black-box systems (surrogate modeling, behavioral analysis, etc)

This role isn’t for you if…

• You've only worked in academic or research settings and haven't applied statistics to real commercial product decisions.
• You prefer narrow, well‑scoped analytical problems over work that spans statistics, regulation, product, and customer context.
• You need complete information before acting, rather than exercising judgment under uncertainty and evolving guidance.
• You'd rather follow established methods than create structure from ambiguity; defining and refining our evaluation process is core to the job.
• You're uncomfortable owning the quality bar and being the one who decides whether an analysis is defensible enough to publish.
• You prefer to stay behind the scenes rather than join high‑stakes customer conversations where clarity and statistical judgement matter.
• You'd rather avoid external scrutiny of your work; ours is shared with enterprise stakeholders and the wider ecosystem, including in public‑facing materials.

What we offer:

• £100-120k+ base salary + meaningful equity in a rapidly growing business
• Private health insurance
• 33 days holiday (incl. bank holidays)
• Hybrid working model (3 days/week in our London office)
• Learning and Development budget of £500 per year

Interview process

Our interview process involves the following stages:

1. Initial screen (30min) - Intro call to align on your background and the role.
2. Founder screen (40min + 40min) - CEO: how you act, decide, and adapt, and how that translates into a high‑agency, fast‑moving startup environment. CTO: your fit against the role's requirements, from statistical rigor to product instinct.
3. Take‑home task - Short analytical case study, designed for 2-3 hours, drawn from the challenges we face; it sets up the on‑site case review.
4. On‑site interview (50min + 50min) - A collaborative case review, and an evaluation scenario, reasoning through its design and uncertainties.
5. Reference checks & Offer - We move quickly from references to a clear offer.

Equal opportunities for everyone

Diversity and inclusion are a priority for us, and we are making sure we have lots of support for all of our people to grow at Warden AI. We embrace diversity in all of its forms and create an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of supporting the responsible adoption of AI systems.

We’re an equal‑opportunity employer. All applicants will be considered for employment without attention to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, veteran status, neurodiversity status or disability status.

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