FloatMe

Data Scientist

• You will turn complex product, customer, and risk data into clear insights, decision frameworks, and durable measurement systems for product, risk, and business partners
• Own end-to-end execution across analysis, metrics definition, experimentation, forecasting, visualization, and decision support
• Define and maintain measurement frameworks for FloatMe products, including customer eligibility, repayment behavior, product usage, loss performance, funnel health, subscription, and long-term customer outcomes
• Partner with Machine Learning Engineers (MLEs) to evaluate model and policy performance, monitor cohorts, identify bias or drift, and connect risk decisions to product and business impact
• Design and analyze experiments, rollouts, and policy changes that shape customer access, repayment outcomes, and product growth
• Approach ambiguous product and risk questions from first principles, using statistical judgment to define the right cohorts, metrics, and decision criteria
• Communicate insights clearly to technical, product, and business stakeholders, including risk partners and senior decision-makers
• Lead technical direction and standards - making and building consensus on key technical decisions, and creating reusable frameworks, measurement templates, and scalable tooling that remove complexity for others
• Drive localized cross-team impact by partnering with senior stakeholders in product, engineering, and risk to align Data Science work with broader strategy

Senior/Staff Data Scientist

Machine Learning Engineer

• You will be a senior individual contributor building and evolving the ML systems behind these products.
• You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration.
• Build, evaluate, and maintain underwriting and decisioning models.
• Design and evolve underwriting decision frameworks, including the modeling, automation, policy logic and amount assignment that manage exposure over time.
• Design and run experiments to evaluate model performance, measure impact on approval rates and loss, margin and inform underwriting policy decisions.
• Develop deep understanding of consumer behavior, repayment dynamics, and portfolio structure, and use that to inform model design and decision logic.
• Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders.
• Develop and maintain the key portfolio KPIs and inventory of periodic analysis to continuously identify risk and growth opportunities.
• Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure underwriting systems reflect business goals and regulatory expectations.

Senior/Staff Data Scientist

Machine Learning Engineer, Underwriting