FloatMe - Senior/Staff Data Scientist
Requirements
• 4+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains. • A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Operation Research). A PhD degree is welcomed. • Advanced proficiency with SQL, Python and experience building clear, decision-oriented data visualizations • Strong product, analytical, and statistical judgment, including the ability to turn ambiguous product, customer, or risk questions into sound analyses, communicate tradeoffs clearly, and support decisions • Experience using AI tools to improve the speed, quality, and durability of analytical work • Fintech background • Consumer finance experience (non-large bank environment) • Advanced modeling techniques • Background in small to medium sized companies
Responsibilities
• 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
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