Senior Data Scientist, Fraud Prevention

About Highnote Founded in 2020 by leaders from Braintree, PayPal, and Lending Club, Highnote is an embedded finance company that specializes in modern card platform management. We offer an all-in-one card issuer processor and program management platform, enabling digital-first organizations to issue and process payment cards, embed virtual and physical card payments, and integrate ledger and wallet functionalities—empowering business growth and profitability. With over $145M raised and a team of 125+ employees across 25+ US states, headquartered in San Francisco, we are committed to creating innovative payment products for the future, driven by our core values of customer obsession, executional excellence, and intentional inclusion. Job Description We are seeking a Data Scientist focused on Fraud Detection to protect Highnote and our clients from financial and reputational risks. The role requires strong analytical skills, machine learning expertise, and knowledge of payments fraud. Success involves identifying and mitigating issuance card transaction fraud and account application fraud, ensuring a secure experience for users while preventing malicious activities. Key Responsibilities : Data Analysis & Pattern Identification : Analyze large datasets to identify fraud trends, attack patterns, and anomalies related to card issuance and account vulnerabilities. Model Development & Implementation : Design, validate, deploy, and monitor machine learning models and rule-based systems to detect fraud in real-time, adapting to new patterns. Feature Engineering : Create impactful features for fraud detection models using internal and external data sources. Strategy Optimization & Experimentation : Conduct A/B tests to evaluate and optimize fraud detection strategies, balancing detection effectiveness and user experience. Performance Monitoring & Iteration : Develop dashboards and reporting tools to track model performance and system health. Cross-Functional Collaboration : Work with Engineering, Product, Risk, and Compliance teams to operationalize models and provide data-driven insights. Communication & Reporting : Present findings and strategic recommendations clearly to technical and non-technical stakeholders. Qualifications Bachelor's or Master's in a quantitative field or equivalent experience. 7+ years in data science or machine learning, with experience in fraud detection within fintech preferred. Proficiency in Python and SQL for data analysis and modeling. Experience developing models/rules for card and account fraud detection, including synthetic ID detection and device fingerprinting. Strong understanding of machine learning concepts, including classification, clustering, and anomaly detection. Experience with the full ML lifecycle and handling imbalanced data. Excellent problem-solving and communication skills. Preferred Skills Advanced degree (Master's or Ph.D.) in relevant fields. Expertise in data modeling, visualization, and understanding of the payments ecosystem. Why Highnote? Opportunity to build from the ground up in a startup environment. Collaborative, inclusive culture with flat hierarchy and direct leadership access. Backed by notable investors and industry leaders. Benefits Flexible PTO, comprehensive healthcare, 401k, parental leave, equity, home office stipend, and competitive compensation. We are committed to diversity and inclusion, welcoming applicants from all backgrounds. Our San Francisco office operates on a hybrid model with core days Tuesday through Thursday, with flexible options available.

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