Senior Data Analyst - Fraud Analytics

Zopa Ltd
London
3 days ago
Create job alert
Overview

looking for a Senior Fraud Analyst to join our growing Fraud Analytics team at Zopa. This role plays a critical part in protecting our customers and the business from fraud, using data, analytics and judgement to drive real-time and strategic decisioning. You will lead complex fraud analysis independently, support incident response, and act as a senior analytical escalation point within the team.


Responsibilities

  • Leading end-to-end fraud analysis using SQL and Python to detect, investigate and mitigate fraud risks
  • Taking real-time analytical input into fraud blocks, controls and interventions
  • Acting as a senior point of escalation during fraud incidents, supporting prioritisation and decision making
  • Optimising fraud rules and supporting the monitoring of machine learning models
  • Improving onboarding and account-level controls to prevent bad actors entering the ecosystem
  • Creating and maintaining fraud reporting and dashboards using Tableau
  • Working closely with Fraud Strategy, Fraud team and related stakeholders

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


We are, Operations, Product and Engineering teams


About you

  • Analytical experience with advanced SQL
  • Python experience for data analysis
  • Proven ability to own complex, ambiguous analytical problems end-to-end
  • Experience translating analysis into clear, actionable recommendations
  • Confidence working with senior stakeholders in a fast-paced environment
  • Ability to remain calm and make sound decisions under pressure
  • Experience in fraud, financial crime or risk analytics is a plus
  • Exposure or an interest in fraud decisioning systems or real-time monitoring tools
  • Prior experience in using live decisioning systems or working with machine learning models
  • Background in Fraud/ Fincrime (or similar setting)
  • Fintech, banking or high-growth tech environments would be ideal

The role and interview process

  1. Recruiter Screening (30 mins) Focus: motivation, problem-solving mindset, technical background, and interest in fraud analytics.
  2. SQL Technical Assessment (1 hour) Focus: writing clean, accurate SQL
  3. Fraud Analytics Case Study (1 hour) Focus: how you approach a fraud problem end-to-end. No prior fraud experience required - we\'re testing thinking, structure, and communication.
  4. Final Interview (60 mins) Focus: competencies, collaboration, decision-making under pressure, and alignment with Zopa\'s values.

Working arrangement

This role is ideal for someone looking for the next step in their analytics career, who wants to continue to build strong technical skills while working on meaningful, high-impact problems. 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!


Diversity and AI in recruitment

We value flexible ways of working. We value face-to-face collaboration and a good work-life balance. Subject to having the right to work in the country of choice, 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 DEI 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 DEI is reflected in our hiring process so please let us know if you require any reasonable adjustments. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst - HOTH, Permanent

Senior Data Analyst

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.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.