Lead Machine Learning Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Machine Learning Scientist based in the United States. Join a forward-thinking financial technology team developing advanced machine learning solutions to strengthen fraud detection and support responsible access to credit. In this hands-on leadership role, you will design, develop, deploy, and optimize predictive models that protect customers and improve financial decision-making. You will tackle complex challenges involving synthetic identities, account takeovers, first-party fraud, and emerging fraud patterns. Working across the entire machine learning lifecycle, you will transform large-scale datasets into production-ready models with measurable business impact. You will collaborate closely with Product, Engineering, and Risk Operations to improve fraud prevention, reduce losses, and optimize approval decisions. The role offers significant technical ownership, opportunities to leverage emerging AI technologies, and the autonomy to drive experimentation and innovation. This is a fully remote opportunity for an experienced machine learning professional passionate about solving real-world problems and expanding access to financial services.

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.