At a Glance
- Tasks: Analyse data to detect fraud and protect consumers on our platform.
- Company: Join Trustpilot, a leading FTSE-250 company with a mission for trust.
- Benefits: Flexible working, competitive pay, 25+ days holiday, and wellness support.
- Other info: Dynamic team culture with opportunities for growth and community engagement.
- Why this job: Make a real impact in the fight against online fraud while developing your skills.
- Qualifications: Analytical mindset with experience in data analytics; SQL skills are essential.
The predicted salary is between 40000 - 50000 £ per year.
At Trustpilot, we're on an incredible journey.
We're a profitable, high-growth FTSE-250 company with a big vision: to become the universal symbol of trust.
We run the world's largest open customer review platform, and while we've come a long way, there's still so much exciting work to do.
Come join us at the heart of trust!
We’re looking for a curious and analytical Fraud Detection Analyst to join our global Fraud & Investigations team.
You’ll analyse data, spot patterns, and write detection rules that help stop fraud in its tracks — playing a vital role in protecting consumers, businesses, and the integrity of our platform.
You’ll work on complex, often ambiguous challenges in the ever-evolving world of online trust.
If you love solving problems, diving into data, and making a real difference - this is your chance.
You’ll be joining a collaborative, inquisitive team that values transparency, fairness, and a good sense of humour.
As part of the wider Trust & Transparency team, we’re driving change across the business—and working to make Trustpilot the universal symbol of trust online.
What you'll be doing
- Hunt for novel fraud on the platform, form hypotheses about how bad actors operate, then prove or disprove them in data that rarely offers a clean answer.
- Invent detection logic from scratch: design the features, criteria, and thresholds that separate fraudulent behaviour from the millions of legitimate reviews it hides among.
- Engineer that logic into production grade detection rules in SQL, carefully structured, logically sound, and built to keep working as fraudsters adapt.
- Own the false positive problem.
Every rule you write can affect real people and businesses, so you'll obsess over precision as much as coverage, building in the exclusions and safeguards that protect legitimate users.
- Investigate escalated cases of platform misuse, and assist with media, legal, and customer inquiries.
- Partner with data scientists to sharpen our detection approaches, and work with engineering to improve the tools and data infrastructure that let your rules scale.
- Act as a resource for other teams by analysing reviewer and business behaviour, and communicate your findings and reasoning clearly to both technical and non-technical stakeholders.
- Work closely with and report to our Manager of Fraud Analytics.
Who you are
- You think like an adversary. When you see a system, you instinctively wonder how someone would game it, and you enjoy the cat-and-mouse of staying ahead of them.
- You're creative with data, not just fluent in it. You can look at a messy, ambiguous dataset and invent a new way to slice it that exposes something nobody had noticed.
- You build, not just report.
You're comfortable writing SQL that goes beyond extraction and joins into genuinely complex, logically layered logic, the kind that becomes a standing detection rule, not a one off query. (We work in SQL, Big Query, and Looker; deep SQL capability matters more than any specific tool.)
- You have strong instincts about the tradeoff between catching fraud and wrongly flagging innocent people, and you treat false positives as a serious cost, not an afterthought.
- You can hold a fuzzy, open ended problem in your head, break it into testable pieces, and stay with it. A lot of this work is ambiguous, and the answer isn't in a playbook.
- Experience in data analytics or a similar analytical field, whether through formal education (Science, Maths, Computer Science, or similar) or equivalent hands on experience.
- Prior fraud or abuse detection experience is welcome but not required, we care more about how you think than what domain you've worked in.
What’s in it for you
- A range of flexible working options to dedicate time to what matters to you
- Competitive compensation package + bonus
- 25 days holiday per year, increasing to 28 days after 2 years of employment
- Two (paid) volunteering days a year to spend your time giving back to the causes that matter to you and your community
- Rich learning and development opportunities are supported through the Trustpilot Academy and Blinkist
- Pension and life insurance
- Health cash plan, online GP, 24/7, Employee Assistance Plan
- Full access to Headspace, a popular mindfulness app to promote positive mental health
- Paid parental leave
- Season ticket loan and a cycle-to-work scheme
- Central office location complete with table tennis, a gaming corner, coffee bars and all the snacks and refreshments you can ask for
- Regular opportunities to connect and get to know your fellow Trusties, including company-wide celebrations and events, ERG activities, and team socials.
- Access to over 4,000 deals and discounts on things like travel, electronics, fashion, fitness, cinema discounts, and more.
- Independent financial advice and free standard professional mortgage broker advice
- Talent acceleration programs: Fast-track your career with our tailored development programs designed to support growth at whatever stage of your career
Still not sure?
We want to be a part of creating a more diverse, equitable, and inclusive world of work for all.
We’re excited to hear about your experiences as well as how you will contribute to our working culture.
So, even if you don’t feel you don't meet all the requirements, we'd still really like to hear from you!
#LI-RS1
Data Analyst (Fraud Detection) in London employer: Trustpilot
Trustpilot is an exceptional employer, offering a vibrant work culture that prioritises collaboration and innovation within the Trust and Safety department. With flexible working options, competitive compensation, and rich learning opportunities through the Trustpilot Academy, employees are empowered to grow and thrive in their careers. The central London location fosters a dynamic environment, complete with engaging office amenities and regular team events, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst (Fraud Detection) in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Trustpilot!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Trustpilot.
✨Apply Directly through Our Website
When you find a suitable opening like Data Analyst (Fraud Detection) at Trustpilot, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Analyst (Fraud Detection) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Trustpilot, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Trustpilot. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Trustpilot
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Trustpilot!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.