Data Scientist

Who we are: We are a leader in fraud prevention and AML compliance. Our platform uses device intelligence, behavior biometrics, machine learning, and AI to stop fraud before it happens. Today, over 300 banks, retailers, and fintechs worldwide use Sardine to stop identity fraud, payment fraud, account takeovers, and social engineering scams.

We have raised $145M from world-class investors, including Andreessen Horowitz, Activant, Visa, Experian, FIS, and Google Ventures. Our culture: - We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture.

#WorkFromAnywhere - We hire talented, self-motivated individuals with extreme ownership and high growth orientation. - We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule.

Location - UK, Germany, Ireland, Spain, Poland, Bulgaria and Lithuania - Remote - From Home / Beach / Mountain / Cafe / Anywhere! - We are a remote-first company with a globally distributed team. So you can find your productive zone and work from there.

About the role As a Data Scientist on our team, you’ll play a key role in helping customers 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 help standardize modeling workflows and collaborate with engineering to streamline backend systems.

This is a hands-on, high-impact role ideal for someone who thrives at the intersection of data science, client-facing problem solving, and real-time risk. 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 - Use data and models to support the development of risk mitigation strategies and interventions while preserving and improving the user experience - Work directly with clients to understand challenges and deliver high-impact, data-driven solutions - Evolve our risk metrics, the supporting datasets, and how we measure the causal impact of initiatives - Collaborate with engineering to scale models into production and optimize performance What you’ll need - 7+ years of experience in data science or quantitative modeling, ideally in risk or fraud contexts - Advanced degree in a quantitative field (Mathematics, Statistics, Computer Science, Engineering, Economics, etc.) - Strong working knowledge of Python, R, Spark, SQL, or equivalent - Sharp critical thinking and creative problem-solving skills with a bias toward action - Proven ability to explain complex technical findings to non-technical stakeholders and clients Benefits we offer: - Generous compensation in cash and equity - Early exercise for all options, including pre-vested - Work from anywhere: Remote-first Culture - Flexible paid time off and Year-end break - Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific - 4% matching in 401k / RRSP - US and Canada specific - MacBook Pro delivered to your door - One-time stipend to set up a home office — desk, chair, screen, etc. - Monthly meal stipend - Monthly social meet-up stipend - Annual health and wellness stipend - Annual Learning stipend Join a fast-growing company with world-class professionals from around the world.

If you are seeking a meaningful career, you found the right place, and we would love to hear from you. To learn more about how we process your personal information and your rights in regards to your personal information as an applicant and Sardine employee, please visit our Applicant and Worker Privacy Notice https://www.sardine.ai/applicant-and-worker-privacy-notice.