Lead Machine Learning Scientist

• Develop advanced fraud detection models: Design, train, validate, and optimize machine learning models to identify synthetic identities, first-party fraud, account takeovers, and new fraud patterns.
• Drive data exploration and experimentation: Analyze large and diverse datasets, engineer predictive features, test modeling approaches, and translate research findings into effective production solutions.
• Manage the end-to-end machine learning lifecycle: Lead projects from problem definition and initial research through prototyping, testing, deployment, monitoring, and continuous optimization.
• Leverage AI-assisted development: Integrate generative AI and modern AI tools into research, coding, feature engineering, experimentation, testing, and model development while validating outputs for accuracy and reliability.
• Optimize fraud and risk decisioning: Monitor model performance, fraud trends, approval rates, and portfolio outcomes to identify improvements that reduce financial losses while supporting responsible business growth.
• Collaborate across technical and business teams: Partner with Product, Engineering, and Risk Operations to integrate models into production systems, resolve implementation challenges, and align solutions with business objectives.
• Provide technical leadership: Independently address complex modeling challenges, make sound technical decisions, share expertise, mentor fellow scientists, and contribute to stronger machine learning practices.
• Measure and communicate impact: Evaluate model effectiveness using relevant performance indicators, communicate findings clearly, and ensure deployed solutions deliver measurable improvements in fraud prevention and customer outcomes.

Requirements

• Professional experience: At least 7 years of hands-on machine learning experience, with a proven record of personally developing, deploying, and optimizing predictive models in production environments.
• Fraud detection expertise: Extensive experience building machine learning solutions for identity fraud, synthetic identities, first-party fraud, and account takeover, ideally within fintech, consumer lending, or financial services.
• Programming and data skills: Advanced proficiency in Python and SQL, including feature engineering, data analysis, model development, experimentation, and performance evaluation.
• Machine learning frameworks and infrastructure: At least 4 years of experience with machine learning frameworks and cloud technologies, including tools such as TensorFlow, PyTorch, Spark, and cloud-based machine learning infrastructure.
• AI-assisted workflows: Experience using generative AI and AI-powered tools for research, coding, analysis, experimentation, testing, and model development, with the ability to critically assess generated outputs.
• Analytical and commercial judgment: Ability to evaluate trade-offs between fraud prevention, approval rates, profitability, regulatory compliance, and customer experience.
• End-to-end ownership: Demonstrated ability to independently scope and solve ambiguous, technically challenging problems, make informed modeling decisions, and deliver measurable results.
• Communication and collaboration: Strong written and verbal communication skills, with the ability to explain complex technical findings, work effectively across functions, and mentor other scientists.
• Education: A bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative discipline, or equivalent practical experience.
• Preferred qualifications: Experience with graph databases, graph-based fraud detection, or network analytics is an advantage.
• Personal attributes: A detail-oriented, curious, and results-driven approach, combined with intellectual flexibility, openness to feedback, and a commitment to continuous learning and collaborative problem-solving.

Benefits

• Salary: Annual base salary ranging from $174,000 to $224,000, depending on experience, skills, qualifications, and other relevant factors.
• Equity: Eligibility for equity awards, providing an opportunity to participate in the company's long-term growth.
• Remote work: A virtual-first working environment that allows employees to work remotely from anywhere in the United States, except Hawaii.
• Flexible schedule: Flexible working hours designed to support autonomy and work-life balance.
• Home office support: Home office stipend to help create a productive remote workspace.
• Healthcare coverage: Premium medical, dental, and vision insurance plans.
• Retirement savings: 401(k) savings plan with matching contributions.
• Family support: Generous paid parental and caregiver leave.
• Time off: Flexible paid time off and generous company holidays, including Juneteenth and a winter break.
• Financial wellness: Access to financial advisors and financial wellness resources.
• Professional growth: Opportunities to tackle challenging machine learning problems, learn from experienced colleagues, experiment with emerging AI technologies, and develop technical expertise.
• Team connection: Company-wide in-person gatherings once or twice a year, complemented by virtual events that connect employees with colleagues and leadership.
• Meaningful impact: The opportunity to build fraud prevention systems that improve financial decision-making, protect customers, and expand access to financial products.