Senior Data Scientist, Customer Success

What you'll do

• Define KPIs and causal levers for onboarding, product adoption, customer health, and retention.
• Own the end-to-end inference approach using randomized experiments, quasi-experiments, or predictive models.
• Build and own predictive models for customer health scoring, churn propensity, and next best action.
• Develop durable customer segmentation patterns to refine targeting and service coverage.
• Translate complex data into executive-level visualizations and actionable business recommendations.
• Design and build automated, monitored data products to enable self-serve analytics for the business.
• Integrate AI solutions into business processes and automate analytical bottlenecks into agentic systems.

What we're looking for

• 4+ years of experience in product, web, customer care, or customer experience analytics, preferably in fintech.
• Advanced proficiency in SQL, big data technologies like Redshift or Spark, and BI tools like Tableau or Qlik.
• Deep expertise in experimentation design and causal inference methods like Propensity Score, DiD, and Synthetic Control.
• Proven track record of framing business questions and taking ambiguous problems through to actionable decisions.
• Experience building and owning predictive models through their lifecycle with accountability for business outcomes.
• Experience building and maintaining automated, monitored, and reproducible data products.
• Bachelor's degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
• Master's Degree (preferred).
• Strong programming skills in Python or R and experience building ML and GenAI models (preferred).
• Experience evaluating AI-native or non-deterministic systems (preferred).
• Familiarity with the accounting, bookkeeping, or professional services domain (preferred).
• Experience solving growth-related problems at financial technology companies serving consumers or SMBs (preferred).