Pharmatalent

What it was

A B2B SaaS talent-advisory platform used by three different groups — recruiters, candidates, and administrators — inside high-compliance European life sciences. The real job was to make complex, ML-driven matching feel simple and trustworthy to a professional recruiter.

The hard part

Three audiences who each wanted different things, a regulated setting that raised the bar on trust and data handling, and a matching engine whose whole value hinged on one thing: whether recruiters believed its results and could push back on them. If they couldn’t, the cleverness underneath was worth nothing.

What I did

I led the client relationship and the product-design work from the first framing through to delivery. That meant advising the senior stakeholders on what to prioritise, and turning the ML story into journeys, interaction rules, and an interface a recruiter could actually understand and control.

The decision that mattered most

We made feedback a first-class part of the flow. Recruiters could correct the results in their own words, right inside the search, so the dual-algorithm matching learned from their expert judgement instead of handing them a ranked list and expecting them to take it.

Where it landed

Recruitment came out about 40% more efficient, and finding the right people got a lot less painful in a corner of European healthcare where the rules leave little room for guesswork.