Data Scientist (Mid - Senior Level)

Utility Warehouse
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist Placement

Company Description

Hi! We're UW. We’re on a mission to take the headache out of utilities by providing them all in one place. One bill for energy, broadband, mobile and insurance and a whole lot of savings!


We’re aiming to double in size as we help more people to stop wasting time and money. Big ambitions, to be delivered by people like you.


We put people first. It’s all about you..


Are you a driven and curious data scientist eager to tackle high-impact business challenges? Join UW's Data team and be at the forefront of understanding our customers.


In this role, you won't just analyse data; you'll answer the critical questions that drive our strategy. We're looking for someone to use advanced data science and machine learning to uncover the 'why' behind customer behaviour, such as identifying complex churn triggers for our energy customers. You will also develop and own the models that quantify key business metrics like Customer Lifetime Value (LTV) and Customer Acquisition Cost (CAC).


This isn't a research role. We need a "full-stack" data scientist who can own the end-to-end ML lifecycle. You will take your ideas from initial Proof of Concept (POC) and exploratory analysis all the way through to building, deploying, and maintaining production-ready models, collaborating closely with our Machine Learning Engineers.


We work together. Your team and the people you will work with…


Our Data teams are small, empowered, and cross-functional, taking full ownership of the solutions they build. We adopt the technologies that best support our goals and continuously raise the bar on how we deliver value.


UW’s Data team is a highly driven, impact-focused group dedicated to understanding customer behaviour and shaping the company’s strategic decisions. The team applies advanced data science and machine learning to uncover key drivers of customer actions and to build business-critical models including Customer Lifetime Value and Acquisition Cost.


What You’ll Do

  • Design and execute advanced statistical and predictive models to understand the key drivers of customer behaviour, retention, and engagement.


  • Develop and refine sophisticated models to measure and predict key commercial outcomes and customer behaviours (e.g., lifetime value, acquisition efficiency), providing critical insights to our marketing and commercial teams.


  • Own the complete development lifecycle for your machine learning models, from data gathering, feature engineering, and POC to model training, validation, and production deployment.


  • Partner with our Machine Learning and Software Engineering teams to deploy your models as scalable, reliable, and robust services using Docker and Kubernetes.


  • Clearly document and communicate complex findings, model results, and actionable insights to both technical and non-technical stakeholders.


  • Keep up to date with the latest research and developments in data science, machine learning, and MLops, and champion new approaches within the team.



Required Skills and Experience

  • Proven experience (e.g., 3-5+ years) in a data scientist role, tackling complex, high-impact business problems like customer behaviour analysis, segmentation, or commercial value modelling.


  • A deep understanding of machine learning theory and practical application (e.g., regression, classification, clustering, time-series forecasting, survival analysis).


  • Solid object-oriented programming skills in Python and hands‑on expertise with the core data science stack (Pandas, Numpy, Scikit-learn, XGBoost/LightGBM).


  • A proven ability to break down vague, complex problems into concrete, solvable steps.


  • Excellent communication and data storytelling skills.


  • You can build strong relationships and manage expectations with diverse stakeholders.



So why pick UW?

We’ve got big ambitions, so there’s going to be plenty of challenges. There are also a lot of benefits:



  • An industry benchmarked salary. We’ll share it during your first conversation.


  • Share Options and Save as You Earn scheme.


  • Enjoy a discount on our services and receive our coveted Cashback Card for free.


  • A matched contribution pension scheme and life assurance up to 4x your salary.


  • Family-friendly policies, designed to help you and your family thrive.


  • Discounted private health insurance, access to an Employee Assistance line and a free Virtual GP.


  • Belonging groups that help UW shape an even more inclusive future.


  • A commitment to helping you develop and grow in your role.



Apply here!

You’ve got this far… Hit apply - we can’t wait to hear from you! Worried you don’t meet all the criteria? We welcome applications from diverse and varied backgrounds, so get your application in and let’s chat!


Beth Rodgers will be your point of contact throughout the recruitment process.


Additional Information

Not sure you meet all the requirements? Let us decide! Research shows that women and members of other underrepresented groups tend not to apply for jobs if they think they may not meet every qualification, when in fact they often do.


We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered.


Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment.


#J-18808-Ljbffr

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.