Senior Data Scientist, Fraud Prevention

About Highnote 

Founded in 2020 by a team of leaders from Braintree, PayPal, and Lending Club, Highnote is an embedded finance company that sets the standard in modern card platform management. As an all-in-one card issuer processor and program management platform, we provide digital-first organizations with the flexibility to seamlessly issue and process payment cards, embed virtual and physical card payments, and integrate ledger and wallet functionalities—empowering businesses to drive growth and profitability.

We’ve raised $145M+ and have grown our team to 125+ employees. Headquartered in San Francisco, we’ve managed to build one of the most advanced payments teams in the industry, with team members in 25+ US states. 

Operating through our core values of customer obsession, executional excellence, intentional inclusion, we’re helping businesses grow for the future by creating the payment products demanded by tomorrow, with the ability to solve for use cases that don’t exist yet.

We are fast-moving, hands-on, and strongly believe everyone deserves a seat at the table. We believe we’re unlocking incredible opportunities that can change the future of payments, as long as we have the right people to make it happen. 

Job Description

As a Data Scientist focused on Fraud Detection, you will be tasked with the critical mission of protecting Highnote and our innovative clients from financial losses and reputational damage. The position requires a blend of rigorous analytical skills, deep machine learning expertise, and a growing understanding of the payments fraud domain. Success in this role means effectively identifying and mitigating threats from issuance card transaction fraud and both first-party and third-party account application fraud, thereby ensuring a secure and seamless experience for legitimate users while thwarting malicious actors. 

Key Responsibilities:

1. Data Analysis & Pattern Identification: Conduct thorough exploratory data analysis on large, complex datasets encompassing transaction logs, application details, user behavior patterns, device information, network data, and other relevant sources. Your goal is to proactively identify emerging fraud trends, sophisticated attack patterns, and subtle anomalies specific to card issuance transactions and account application vulnerabilities (including first-party and third-party identity fraud).
2. Model Development & Implementation: Design, develop, rigorously validate, deploy into production, and meticulously monitor machine learning models. These may include techniques like logistic regression, decision trees, random forests, gradient boosting machines, clustering, or anomaly detection (outlier detection) algorithms. Complement these models with well-defined rule-based systems where appropriate. The objective is to detect and prevent fraudulent activities in real-time or near real-time, adapting models as new fraud patterns emerge.4
3. Feature Engineering: Conceptualize, create, and refine impactful features for fraud detection models. This involves leveraging diverse internal data streams from Highnote's unified platform and potentially integrating external data sources. Strategy Optimization & Experimentation: Design, execute, and analyze A/B tests and other controlled experiments to rigorously evaluate the performance of different fraud detection strategies, specific rules, and machine learning models. The focus is on optimizing the trade-off between maximizing fraud prevention effectiveness and minimizing the impact on legitimate user transactions and onboarding experiences (reducing false positives).
4. Performance Monitoring & Iteration: Establish and maintain comprehensive monitoring systems, dashboards, and reporting mechanisms to continuously track model accuracy, rule precision/recall, key fraud metrics (e.g., detection rates, false positive rates), and overall system health.Cross-Functional Collaboration: Work closely and effectively with Engineering, Product Management, Risk Operations, and Compliance teams. This collaboration is essential for understanding business requirements, operationalizing models and rules efficiently within production systems, integrating necessary data pipelines, and providing actionable, data-driven insights to inform broader risk strategies.
5. Communication & Reporting: Clearly and concisely communicate complex analytical findings, model behaviors, performance metrics, and strategic recommendations to diverse audiences, including technical peers, operational teams, product managers, and executive leadership.

Qualifications

• A Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Data Science, Mathematics, Engineering, Physics, or equivalent practical experience demonstrating strong analytical foundations.
• Minimum of 7+ years of hands-on professional experience in data science or machine learning roles. Experience within fraud detection, risk management, payments, or the broader fintech industry is required.
• Strong proficiency in Python for data analysis, statistical modeling, machine learning implementation, and scripting automated processes. Proficiency in SQL for efficient data extraction, transformation, and analysis from large, complex relational databases or data warehouses.
• Direct, hands-on experience developing and deploying models or rule sets specifically targeting card issuance and acceptance transaction fraud (e.g., detecting unauthorized use, velocity attacks) or account application fraud (e.g., first-party fraud, third-party identity theft, synthetic identity detection). Experience with synthetic ID detection and device fingerprinting  is particularly valuable.
• Solid understanding and practical application experience of core machine learning concepts and algorithms, particularly classification (e.g., logistic regression, SVM, tree-based methods), clustering, and anomaly detection techniques relevant to fraud.
• Demonstrated experience with the end-to-end machine learning model lifecycle: data collection and cleaning, exploratory data analysis, feature engineering and selection, model training and tuning, validation (including handling imbalanced data), deployment strategies, and ongoing performance monitoring.
• Excellent analytical and problem-solving skills, with a proven ability to dissect complex business problems, formulate quantitative approaches, and deliver practical, data-driven solutions.
• Strong communication and interpersonal skills, capable of clearly explaining intricate technical details and model outcomes to both technical and non-technical stakeholders.

Preferred Skills

• An advanced degree (Master's or Ph.D.) in Computer Science, Statistics, Data Science, or another relevant quantitative discipline.
• Strong expertise in data modeling with a deep understanding of both structured and unstructured data formats, including common techniques for data transformation and normalization
• Proficiency with data visualization tools (e.g., Tableau, Looker, Matplotlib, Seaborn) to create informative dashboards and communicate insights effectively.
• A deeper understanding of the broader payments ecosystem, including card network operations (Visa, Mastercard), ACH processes, payment processors, and relevant regulatory frameworks (e.g., KYC/AML).

Why Highnote?

• We’re a startup that allows for our employees to truly build from the ground up and impact every layer of our organization. 
• We’re a team of payments obsessed individuals. While some of us come from the fintech world, some of us don’t. We value the varied backgrounds and the diverse perspectives of our employees. 
• We’re small on hierarchy and big on growth. We’re a flat organization that allows everyone to have direct exposure to our leadership team. We are looking for builders who thrive in ambiguity. 
• We’re backed by Oak HC/FT, Costanoa Ventures, Adams Street Partners, Westcap, and Pinegrove Venture Partners. Angel Investors include Bill Ready (CEO at Pinterest) and Renaud Laplanche (Co-Founder & CEO of Upgrade).

Highnote benefits

• Flexible Paid Time Off 
• 100% healthcare coverage + 75% coverage for dependents
• 401k program
• Paid Parental Leave: Up to 16 weeks paid leave for the birth parent, and up to 6 weeks paid leave for the non-birth parent
• Equity in Highnote
• Stipend to build out your home office; internet and phone reimbursement
• At Highnote we have built a total rewards philosophy that includes fair, equitable, geo-based compensation that is performance and potential based. Our compensation packages are competitive based on robust market research and are a combination of a cash salary, equity, and benefits. In compliance with the Equal Pay for Equal Work Act, the annual salary range for applicants is $180,000-$220,000. 

Highnote is a diverse and inclusive company committed to growing a diverse and inclusive team. We invite people from all backgrounds and identities to apply. We do not discriminate based on gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or any other characteristics protected by US federal state or local laws, or the laws of the country or jurisdiction where you work. Additionally, we encourage everyone to share which pronouns you wish for us to use when addressing you (i.e.: she/her, he/him, they/them, etc).

Please note that positions located in San Francisco are hybrid and include core working days of Tuesday, Wednesday, Thursday in office. We provide flexible work options based on distance from our downtown SF office. Highnote believes in the power of face-to-face, personal connection. As a result, we prioritize in-person candidates.