← All work
HR Tech · 2023–2024

From Data to Decisions: Designing a DE&I Intelligence Tool for Hiring Teams

Lead Product Designer Scale-up, HR Tech Squad of 3 → hackajob

Hackajob is a UK scale-up hiring platform connecting employers with technology talent through skills-based hiring. As a Staff Product Designer, I led discovery and product design across new initiatives, validating ideas with customers before investing in development.

I reframed a dashboard brief into a commercially validated decision support tool that shipped to 20 enterprise clients in its first month and got featured in Forbes.

20
Enterprise clients in month one, including Sainsbury's
40%
Of customers indicated willingness to pay
60%
Of clients made meaningful adjustments to hiring strategy
800
Candidate surveys synthesised with AI to surface behaviour patterns

The Challenge

The brief was simple: build a dashboard. The real problem was harder.

Hackajob collected rich data across the hiring funnel but had no way to surface it meaningfully. Nobody had defined what decisions that dashboard should actually enable.

Before a pixel was pushed I stopped the squad and asked a different question. Not "what data do we have?" but "what decisions are our customers actually struggling to make?"

That single reframe changed the product, the positioning, and the commercial outcome.

A Decision Engine, Not a Dashboard.

Lets turn this into something that actually changed what hiring teams did on Monday morning.

Proving appetite

Before investing in building a new analytics product, I wanted to validate demand.

I introduced fake-door experiments alongside customer interviews and large-scale candidate research to understand whether customers actually wanted deeper DE&I insights—and which problems they would pay to solve.

Rather than measuring clicks, the goal was to reduce product risk before committing engineering effort.

Evidence → Product strategy

Moderated & unmoderated testing, and 800 candidate surveys with AI sysnthesis.

The fake-door experiments gave us confidence that customers valued these insights enough to justify investment. Rather than treating analytics as a reporting feature, we reframed the product around helping companies improve hiring outcomes through measurable behavioural change.

View Data

Reframing the problem from a dashboard to a decision engine was the pivot.

The value

Reduced product risk.

  • Fake-door testing validated demand.
  • Research shaped the roadmap.
  • The resulting product launched with enterprise adoption and measurable customer value.

Establishing a Research Practice

I introduced a dual-track discovery model to the organisation.

  • Lean UX, moderated & unmoderated usability testing, fake door tests
  • 800 candidate surveys synthesised with AI — a new workflow in 2023
  • Sessions with 5 industry thought leaders and HR consultants
  • Weekly CS collaboration sessions & customer interview cadence

I shipped pipeline health views, DE&I breakdowns, and candidate voice.

A reasons for decline feature came directly from customer research. Companies told us they had no way to understand why good candidates were walking away. I designed the capture flow, built it into the candidate experience, and surfaced the verbatim feedback alongside AI-grouped thematic analysis inside the dashboard.

This was not a data-point-only dashboard. Every metric had a decision behind it.


hackajob engage

Results

Featured in Forbes. 20 enterprise clients in Month one.

What I'd Do Differently

Validating demand is just the start. The real challenge is turning evidence into long-term product strategy.

  • The discovery work gave us confidence to build Engage, but looking back I'd have pushed harder to evolve it beyond a reporting dashboard.
  • The signals we uncovered weren't just useful for visualisation, they were the foundation for recommendation systems, intelligent hiring guidance and proactive product experiences. I would have made that strategic case earlier, backed by the evidence we had already gathered.

What Happened Next

The discovery work fundamentally changed how I think about AI and product design.

  • Hackajob taught me that valuable products aren't built by adding AI to existing workflows, they emerge when structured data, user intent and behavioural signals come together to help people make better decisions.
  • That thinking carried into my work at Deel where I explored how connected performance data could support managers rather than simply report on teams, and later into Matcheros where AI became part of the hiring workflow from day one.
← All work