Data Scientist - Fraud & Risk Analytics

We're looking for a data-driven professional to help us measure, understand, and improve the performance of our risk strategies — and to stay ahead of evolving fraud threats by designing and deploying data-driven solutions with real-world impact. You'll work directly with clients to understand their unique fraud challenges, rapidly prototype proof-of-concept models, and build scalable, production-ready solutions using machine learning and graph analytics. You'll also analyze complex datasets, design metrics, build dashboards, and collaborate closely with stakeholders across the business to drive decision-making and optimize outcomes.

What you'll be doing

• Champion a data-first approach across internal teams and client engagements, promoting clarity and impact
• Build and deploy machine learning models to prevent fraud across diverse fintech use cases, from proof-of-concept through to production
• Develop and track metrics to measure and monitor the performance of our risk products and the effectiveness of risk management strategies
• Conduct in-depth analyses to uncover insights contributing to fraud reduction and higher approval rates for our clients
• Work directly with clients to understand their fraud challenges and translate complex data insights into clear, actionable recommendations
• Use data and models to support the development of risk mitigation strategies and interventions while preserving and improving the user experience
• Create and automate self-serve dashboards leveraging BI tools
• Collaborate with engineering to scale models into production, optimize performance, and support data instrumentation
• Partner with cross-functional teams (Business, Product, and Engineering) to translate business requirements into data-driven solutions

What you'll need

• 7+ years of experience in data science, quantitative modeling, or a data-focused role (product analytics, business analytics) with demonstrated high impact in fraud or risk contexts
• Strong hands-on experience with Python/R and SQL is essential, with Spark being a nice to have
• Expertise in BI tools such as Tableau, Sigma, or Metabase
• Proven ability to structure and analyze complex data using techniques like EDA and cohort analysis, and communicate findings effectively to both technical and non-technical audiences, including clients
• Sharp critical thinking and creative problem-solving skills with a bias toward action
• Proficiency in defining, tracking, and communicating performance metrics