Related skills
sql python data visualization machine learning excel📋 Description
- Develop and deploy predictive models across the credit lifecycle to improve approval quality and loss performance.
- Translate model outputs into actionable credit strategy, including approval cutoffs and rules.
- Analyze portfolio performance to identify drivers of deterioration and recommended actions.
- Evaluate tradeoffs between approval rate, loss, and profitability to optimize portfolio performance.
- Distinguish fraud risk from credit risk to improve early default performance and reduce losses.
- Design and execute experiments (A/B tests, champion/challenger) to evaluate strategies.
🎯 Requirements
- Degree in data science, applied math, statistics, economics, computer science, or related field.
- 5-7 years of professional experience in data science or analytics in FinTech or online lending.
- Advanced proficiency in Python for data analysis and predictive modeling.
- Proficiency in SQL, Excel, and data visualization tools.
- Strong knowledge of applied statistics and ML methods including linear models, trees, boosting, and ensembles.
- Knowledge of optimization, stochastic processes, experimental design, A/B testing, and bootstrapping.
🎁 Benefits
- Comprehensive healthcare including medical, dental, and vision coverage
- Generous paid time off, including PTO, sick time, and 13 company holidays
- 401(k) with company contribution
- Participation in annual discretionary bonus plan
- Regular team and company gatherings
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