Lead Data Scientist

Zopa Bank Limited
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Our Story


Hello there. We’re Zopa.


We started our journey back in 2005, building the first ever peer-to-peer lending company. Fast forward to 2020 and we launched Zopa Bank. A bank that listens to what our customers don’t like about finance and does the opposite. We’re redefining what it feels like to work in finance. Our vision for a new era of banking puts people front and centre — we’ve built a business that empowers everyone to aim high, every day, to move finance forward. Find out more about our fantastic offerings at Zopa.com!


We’re incredibly proud of our achievements and none of it would be possible without the amazing team here. It’s not just industry awards we’re winning, we’ve also been named in the top three UK’s Most Loved Workplaces.


If you embrace unconventional challenges, are unafraid to think differently and are driven to make an outsized impact, you’ll thrive here at Zopa, so join us, and make it count. Want to see us in action? Follow us on Instagram @zopalife


At Zopa, data and the application of machine learning is at the heart of what we do and the products we bring to market. Within consumer financial services we have pioneered modern data science techniques using advanced ML models for more than 7 years.


Today more than 98% of our lending decisions are driven by ML models - so it’s safe to say it is seriously impactful work!


As a Lead Data Scientist at Zopa, you will be leading high impact projects related to data and modelling, across a broad range of topics such as marketing, customer engagement, credit risk, fraud detection and pricing.


You will own the full lifecycle of your projects, including the discovery of business opportunities through statistical analysis, data curation and processing, feature engineering, development of machine learning models, deployment to production, and model monitoring. You will engage with senior stakeholders across the company, influence critical business decisions, and make direct impacts on our products and millions of customers.


On a daily basis, you will work closely with product managers, analysts, data engineers and software engineers to make progress on your project. You will also support other data scientists by knowledge sharing, code review, collaboration on common utilities and analytical infrastructure.


A day in the life..
  • Lead high impact projects related to data and modelling.
  • Own the full lifecycle of your project, including the discovery of business opportunities through statistical analysis, data curation and processing, feature engineering, development of machine learning model, deployment to production and model monitoring.
  • Engage with senior stakeholders across the company, influence critical business decisions, and make direct impacts on our products and millions of customers.
  • Work closely with product managers, analysts, data engineers and software engineers to make progress on your project.
  • Support other data scientists by knowledge sharing, code review, collaboration on common utilities and analytical infrastructure.

About you..
  • You love data. You are passionate about tackling real world problems with data. You have proven track record of solving complex data problems and delivering business value.
  • You are a scientist. Always curious and eager to learn. You have an inquisitive mind to delve under the surface and challenge status quo. You are fearless in innovation, for the good of our customers and the world.
  • You are a great communicator. You enjoy influencing decision makers with insights from data. You can foster mutual understanding and trust with stakeholders of different perspectives.
  • You are a team player, striving for the success of the team and collaborate with an open mind. You have the can-do attitude with strong commitment to get the job done.
  • You have excellent Python skills, with a good understanding of the best practices in Software Engineering. You are familiar with tools such as Git, Docker, CI/CD, REST API.
  • You have in-depth knowledge of machine learning algorithms (e.g. logistic regression, random forest, gradient boosted trees, neural networks, k-means, etc) and statistics (e.g., Monte Carlo, hypothesis testing, confidence intervals, maximum likelihood, bootstrap, Bayesian inference).

A bonus if..
  • Experience with Causal Inference modelling.
  • Domain knowledge of the financial services industry, especially consumer lending or credit risk.
  • Experience in building and deploying Generative AI based processes and systems.
  • You have people management experience.

At Zopa we value flexible ways of working.


We value face-to-face collaboration and a good work-life balance. This hybrid role requires you to come to our London office 2-3 days a week.


You’ll also have the option of working from abroad for up to 120 days a year! But no matter where you are, we’ll make sure you’ve got everything you need to thrive, both in your work and home life, from day one.


*Subject to having the right to work in the country of choice


Diversity Statement

Zopa is proud to offer a workplace free from discrimination. Diversity of experience, perspectives, and backgrounds leads to better products for our customers and a unique company culture for our people. We are made up of nearly 50 nationalities, have a DE&I forum made up of Zopians wanting to make a difference and we are proud of our culture where everyone can bring their full self to work. Our approach to DE&I is reflected in our hiring process so please let us know if you require any reasonable adjustments.


#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 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.