Senior UX Researcher (Data Science / AI)

Pontoon Solutions
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer (Platform) - Bristol

Senior Machine Learning Engineer (Platform) - Exeter

Senior Machine Learning Research Engineer

Senior Data Scientist

Senior Data Scientist

Senior UX Researcher (Data Science / AI)

Retail

3 days per week in London

6 months

£600 per day


In short:We're at the beginning stage of building out a new Data Science-driven platform and we need a strong tech-focused UX Researcher to ask the right questions of our tech teams (Qualitative).

Anyone with Data Science, AI or Platform experience would be good in this role.


In full:

We are looking for a Senior UX Researcher to advocate the needs of our head office colleagues and Data Scientists to the wider business, helping identify and solve user experience challenges to enable our users to collaborate more efficiently & make better business decisions.


You will work with a multidisciplinary team of UX Designers, Product Managers, Data Scientists and Engineers to improve the user experience for head office colleagues, partnering with Design Managers and the UX Designer to inform the research agenda for different business and data science domains.

You are a natural problem solver who is highly skilled in a range of research methods, with demonstrable experience turning research findings into actionable recommendations that drive user-centred design and product outcomes.

You are at ease with complex challenges, you know how to investigate and map the use of systems that involve multiple services, and how to present your findings with clarity and rigour. You excel at engaging with stakeholders and senior leadership to ensure UX research can inform design, product, and business decisions.

As our ideal candidate, you have a passion for understanding people, their motivations, and behaviours, and the challenges they face. You have excellent observational and analytical skills, strong design thinking expertise, the ability to collaborate with other teams effectively, and a relentless determination to do what is right for our colleagues & suppliers. You will be responsible for identifying user needs and jobs to be done, spotting opportunities and delivering actionable insights that inform the development of compelling user-centred experiences and digital products.


Core Responsibilities

• Own and lead UX research in the Data Science team. Our main focus is to make it easier for our colleagues to simulate different ‘what if’ scenarios. As such, our two big user groups are our colleagues in the Head Office and Data Scientists.

• Scope, plan and execute research throughout the design and development lifecycle, from early strategic direction through post-release validation.

• Work closely with cross-functional product team to develop a research strategy that includes generative and evaluative research, and ensure the right data is being collected to positively impact the product direction.

• Synthesise and communicate insights from research with clarity and effectiveness to a broad range of stakeholders. Partner with cross-functional teams to ensure research insights are incorporated into product design strategy to drive measurable outcomes. Support the Simulation team in establishing successful user-centred processes and ways of working.

• Share research expertise and knowledge with others and encourage the wider team to actively participate in UX Research, to improve organisational capability and challenge team thinking.


Key skills and experience

You’ll need to have demonstrated experience of:

· University degree of 2:1 or higher in human-computer interaction (HCI), a related field, or equivalent years of professional experience.

· Expert-level knowledge in the field of UX research with multiple examples of experience of working as a UX researcher on digital products, preferably focusing on discovery and strategic research and shaping research priorities for a product area.

· A talent for storytelling that manifest in excellent communication, presentation, and interpersonal skills; the ability to communicate complex concepts clearly and persuasively across different audiences and varying levels of seniority.

· Strong track record of research work impacting design and product strategy and development, and delivering measurable outcomes.

· Considerable knowledge and hands-on experience with a wide range of qualitative and quantitative research methods and techniques (such as user interviews, user profiles, user needs and jobs to be done, journey mapping, usability testing, surveys, card sorting, tree testing, competitor analysis).

· Ability to collaborate with a wide set of stakeholders including designers, product managers, developers, data scientists and analysts.

· Preferable: experience of working in an agile product development environment and applying lean UX research methods.

Candidates will ideally show evidence of the above in their CV.


Please be advised if you haven't heard from us within 48 hours then unfortunately your application has not been successful on this occasion, we may however keep your details on file for any suitable future vacancies and contact you accordingly. Pontoon is an employment consultancy and operates as an equal opportunities employer.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.