Data Scientist - Fraud Detection

Are you looking for a new challenge?

Fancy helping us shape the future of motor insurance?

Prima could be the place for you. 

Since 2015, we’ve been using our love of data and tech to rethink motor insurance and bring drivers a great experience at a great price. Our story began in Italy, where we’ve quickly become the number one online motor insurance provider. In fact, we’re trusted by over 5 million drivers. And now we’re expanding to help millions more drivers in the UK and Spain.

To help fuel that growth, we need a Data Scientist to join our Anti Fraud Team.

In this role, you’ll have the opportunity to tackle fraud, one of the most impactful and fast-evolving challenges in insurance. You will do this from a global perspective. In fact, the team is responsible for detecting fraudulent claims across all Prima markets: the UK, Spain and Italy.

You’ll collaborate with engineers, claims handlers and business leaders to improve the detection of fraud, directly influencing real-world fraud decisions with impact on the company’s bottom line measured in millions. This is a highly practical area of work where strong business judgment is just as important as technical expertise.

Excited to make an impact? Here are the details

What you’ll do

Design and improve fraud detection systems that prioritize high-risk claims, optimizing the trade-off between detection accuracy and operational efficiency

Collaborate across teams and functions, working closely with fraud analysts, engineers, and product managers

Develop deep expertise in fraud dynamics, identifying emerging fraud patterns, understanding how they evolve across the insurance lifecycle, and translating these insights into effective detection and prevention strategies, for example identifying suspicious networks of related claims, synthetic entities, or anomalous behavioral patterns

What we’re looking for

Extensive experience (3+ years) in data science or applied machine learning, leading end-to-end initiatives in complex technical domains and delivering measurable business impact

Strong analytical and quantitative mindset able to translate data into insights, assess model performance, and guide decision-making with evidence and clarity

Structured problem solver skilled at framing ambiguous challenges, defining success metrics, and communicating results to both technical and non-technical audiences

Strong Python programming skills, especially in the data domain, and fluent use of Git

Knowledge of Spark, SQL or any other data-oriented programming language and proficiency with standard data wrangling and modeling libraries (Pandas, Sklearn, Keras, XGBoost, LightGBM, etc.)

Nice-to-Have

Experience in analytical fraud detection in the Insurance, Finance or Law Enforcement domain

In-depth knowledge of graph databases (Neo4j, AWS Neptune) and analytics (NetworkX, GNN) or explainability applied to anomaly detection

Knowledge of BI tools (Tableau or similar)

Why you’ll love it here   We want to make Prima a happy and empowering place to work. So if you decide to join us, you can expect plenty of perks.   💵 Competitive compensation: We offer a performance-based bonus of up to 5% of the fixed salary for this position.    🤸 Work Your Way:  Enjoy hybrid working, with a mix of home and office days. Plus, for up to 30 days per year, work from anywhere.

🏁 Grow with us: We may move fast at Prima, but we move together. Get access to learning resources, mentorship and a growth plan tailored to you.

🌈 Thrive and perform: Your best work begins when you feel your best. Enjoy private healthcare, gym discounts, wellbeing programs and mental health support.

Think you’re a match? Apply now.

 

At Prima, we celebrate uniqueness. If you don’t meet every requirement but are passionate about this role, we still want to hear from you. Innovation thrives on diverse perspectives.

Prima is proud to be an equal opportunity employer. Need accommodations during the process? Email us at accessible.recruiting@prima.it. Let’s build the future of insurance, together.