Clair

Data Scientist

• Develop and maintain credit risk and repayment models that directly power Clair’s underwriting and decision systems.
• Design and engineer predictive features using transaction-level data, including the prediction of user income flows, spending, and repayment behaviors.
• Collaborate with engineering teams to deploy, monitor, and optimize models in production environments using AWS and Snowflake ML Platform.
• Build prototypes of new modeling methodologies and present them to stakeholders for evaluation and feedback.
• Investigate and analyze credit performance, identifying root causes of model drift or unexpected repayment behavior.
• Partner cross-functionally with product, risk, and engineering teams to turn data insights into product improvements and credit strategy refinements.
• Communicate technical concepts clearly to both technical and non-technical audiences.
• Stay current on best practices and emerging techniques in credit modeling, explainable AI, and financial ML systems.

Senior Data Scientist, Credit Underwriting

Data Analyst

• Leverage data to drive user growth, identify trends, and influence product and business decisions
• Partner with business stakeholders across the organization to answer questions and test hypotheses
• Define and manage key metrics that inform strategic product, growth, and operational decisions, ensuring alignment with business goals
• Put data at the fingertips of stakeholders and enable them to independently explore and utilize data
• Build efficient data ingestion and transformation pipelines
• Develop intuitive dashboards, memos, slides, and other visualizations that tell stories and inspire action
• Collaborate to brainstorm ideas, verify analyses, and review the work of your peers to ensure accuracy and quality

Senior Data Analyst, Product Insights

Financial Planning and Analysis

• Build, maintain, and defend Clair’s consolidated operating model
• Run the annual budget process end-to-end. Bottoms-up plans with every department, reconciled to top-down targets
• Maintain weekly rolling forecasts. You’re the first person to know when something is off and you turn that into action, not just insight
• Variance analysis that says something. Not "Revenue was $50K below plan" but "Revenue was $50K below plan because partner X’s conversion dropped 15% after their app update, and here’s what we should do"
• Own the reporting cadence: monthly reviews, QBRs, board materials
• Build the models behind fundraising: investor decks, data rooms, due diligence answers
• Model new product economics before we build anything. What are the unit economics? What’s the break-even?
• Pricing and take-rate analysis for new and existing partnerships
• Be the VP of Finance’s go-to analytical partner on big-picture questions
• Product team says engagement is up. Partnerships says a new population is onboarding. Data science says default rates are shifting. You’re the one who turns those inputs into a coherent financial picture
• Translate model outputs into plain English for people who don’t live in spreadsheets
• Make sure the model matches reality. Partner with accounting on close. If actuals and forecast diverge, figure out why
• Build processes that scale. Automate the stuff that should be automated. Know when a human needs to look at the number. Evolve our processes and tools to be AI-native and generating maximum operating leverage
• Own the tooling: Pigment, dashboards, reporting infrastructure

Senior Data Scientist, Credit Underwriting

Senior Data Analyst, Product Insights

Head of Strategic Finance