Senior Data Scientist - Risk and Fraud Management

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

We are a leading issuing and acquiring payments processor dedicated to providing secure and efficient payment solutions. Our Risk and Fraud Management team plays a critical role in ensuring the integrity and trustworthiness of our services by proactively identifying and mitigating fraud while delivering exceptional experiences for our customers. We are seeking an experienced, hands-on Senior Data Scientist to lead our efforts in combating fraud and managing risk across our payment processing platforms. This individual contributor role is highly technical and strategic, requiring a deep understanding of fraud detection in the payments industry, coupled with strong business acumen. As the team grows, you will have the opportunity to build and lead a team of talented professionals.

Key Responsibilities:

1. Data Analysis and Model Development:
   
• Analyze large and complex datasets to uncover patterns, trends, and insights related to fraud and risk.
• Develop, validate, and deploy machine learning models and algorithms to detect and prevent fraudulent activity.
2. Technical Implementation:
   
• Collaborate with engineering teams to operationalize fraud detection models, ensuring scalability and performance.
• Build and maintain pipelines for real-time fraud detection and risk scoring.
3. Strategic Leadership:
• Proactively identify emerging fraud patterns and design strategies to address them.
• Provide thought leadership on best practices in fraud prevention and risk management.
4. Industry Expertise:
• Leverage in-depth knowledge of payment systems, credit card fraud, and fraud mitigation tools.
• Stay abreast of industry trends, tools, and techniques to continuously enhance fraud management capabilities.
5. Collaboration and Communication:
• Partner with business stakeholders to understand fraud-related challenges and deliver actionable insights.
• Communicate findings and recommendations to technical and non-technical audiences, including executives.
6. Team Development:
• Act as a mentor and thought leader within the organization, with the potential to build and lead a dedicated team over time.

Qualifications:

• 10+ years of experience in data science, with significant expertise in fraud detection and risk management within the payments industry.
• Hands-on experience developing and deploying machine learning models, including real-time systems.
• Proficiency with programming languages such as Python, R, or Scala, and machine learning frameworks.
• Strong SQL and data manipulation skills.
• Familiarity with fraud detection tools and technologies used by payment providers, such as rule-based systems, behavior analytics platforms, and consortium data.
• Excellent problem-solving skills and a proactive mindset.
• Outstanding communication and visualization skills.
• Strong and demonstrated experience with Looker 
• Knowledge of risk scoring, identity verification, and anomaly detection techniques.
• BS in Data Science, Computer Science, Statistics, or a related quantitative field.

Preferred Skills:

• Experience with big data tools and technologies such as Big Query, or similar.
• Exposu