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Descrial is hiring its first UX Researcher to build the evidence behind our product and design decisions. Our users are investment professionals; access must be built through client relationships and studies cover workflows and AI-assisted work.
You will report to the Head of Product Design and collaborate with Product and Engineering. You will design qualitative and quantitative studies, manage participant access and data handling in confidential financial contexts, delivering usable evidence
We're hiring our first UX Researcher to build the evidence behind Descrial's product and design decisions. Our users are investment analysts, associates and partners. They're short on time, deep in expertise and working with confidential data, so getting useful access to them is part of the job.
Descrial is a startup inside a twenty-year-old company
TresVista has run the analytical work behind private capital since 2006. 2,000 people, 400+ clients including some of the biggest names in the industry, no outside capital and no outside board. That was the first chapter. And we enjoyed getting here!
The next chapter is harder and more interesting
We are moving from services to software, from one firm's workflows to an industry's, and from executing investment decisions to shaping how they get made. Thirty of us in King's Cross, small and senior, with day one founder intensity.
At its fullest expression, Descrial changes how every serious investment firm decides.
You report to Daniel Parkes, Head of Product Design, and work with Product and Engineering. You build participant access through client relationships and study both the workflows professionals follow and how they judge AI-assisted work.
You design qualitative and quantitative studies, from discovery interviews and contextual inquiry to usability testing and instrumented behavioural analysis. You build participant access, consent and data handling suited to confidential financial contexts, and maintain a searchable insight repository. Your methods need to produce usable evidence in days and fit the release rhythm.
You turn findings into prioritised design and roadmap choices, keeping the evidence traceable and arguing the trade-offs where decisions are made. Good synthesis gives the team a clear reason to change direction or stay with a choice.
You study trust, verification behaviour and tolerance of failure in LLM-powered features. With Product and Engineering, you define what good output looks like and contribute user-grounded golden examples and failure taxonomies to the evaluation sets that gate AI releases.
This will be difficult.
It demands pace and accuracy. We are building autonomous systems inside high-stakes live businesses, where decisions carry real consequences and impact and waiting for everyone to agree is not always an option. The systems will sometimes get things wrong. In this phase the London team needs to create the evaluations, visibilities and safeguards that catch mistakes early, reducing them over time and stopping the same failures happening again.
If you want to build the foundations of an enterprise grade technology platform to service a multi-billion $ serviceable market opportunity, and share in that growth, this is your chance to join us.