Data Scientist

About Middesk:Middesk is building the data and intelligence infrastructure that helps businesses work together with confidence. We started by creating a comprehensive platform for understanding businesses, bringing together authoritative and proprietary data to help customers verify business identities, onboard customers faster, and manage risk throughout the customer lifecycle.Today, Middesk is used by more than 700 banks and fintechs, and in 2025 we verified more than 7 million companies. We've also expanded beyond business verification to help companies form, register, manage, and maintain their businesses, supporting more than 50,000 companies in setting up over 100,000 accounts required to hire employees, run payroll, and stay compliant.Middesk came out of Y Combinator, and is backed by Sequoia Capital, Accel, Insight Partners, and Canapi. We're proud to be named on the Forbes Fintech 50 and Best Startup Employers lists. About The Role:We’re building AI-driven applications that simplify customer workflows, starting with business onboarding. With our proprietary identity data and deep domain expertise, we’re in a strong position to expand into a broader set of intelligent, risk-aware products.We’re looking for a hands-on engineer to help build the foundation for these systems. This role is less about inventing new ML algorithms and more about applying the right techniques to messy, real-world problems. You’ve worked in fraud, risk, or trust domains, and you understand how bad actors behave, how data breaks, and how to still ship reliable systems anyway.This is a highly technical, hands-on role with broad influence over how we design, build, and scale data-driven systems at Middesk.We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF/NYC office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.What You’ll Do:Build fraud & risk systemsDesign and ship production systems that detect and prevent fraud across KYB, trust & safety, and compliance workflows.Work with messy, real-world dataTackle problems with extreme class imbalance, sparse signals, evolving adversarial behavior, and limited ground truth.Leverage relationships in dataApply graph-based approaches and entity resolution techniques to uncover hidden connections and improve risk detection.Improve signal & labelingUse a mix of heuristics, weak supervision, and modern AI tools (including LLMs where appropriate) to generate better features and labels.Help scale our infrastructurePartner with engineering to build and evolve systems for feature generation, model training, and production deployment across multiple use cases.What We’re Looking For:5+ years of experience in fraud, risk, or trust & safetyYou’ve worked on real-world fraud or abuse problems and understand the domain deeply.Experience building and shipping production systemsYou’ve deployed models or data-driven systems that power external-facing products.Strong foundation in applied ML or data systemsComfortable working on classification problems with real-world constraints like imbalanced data, sparse signals, and changing patterns.Experience with graph or relational data approachesFamiliarity with knowledge graphs, network analysis, or entity linking is strongly preferred.Hands-on and pragmaticYou focus on impact over perfection and know how to balance speed, accuracy, and maintainability.Compensation Range: $175K - $210KLocationSan Francisco; New YorkEmployment TypeFull timeLocation TypeHybridDepartmentData ScienceCompensation$175K – $210K • Offers EquityMiddesk is committed to equitable and competitive compensation, including equity and benefits. This salary range may be inclusive of several career levels at Middesk and will be narrowed during the interview process. Actual compensation will depend on numerous factors such as: experience, knowledge and skills, qualifications, location, and other job-related factors.