Join Us as a Lead Credit Risk Data Scientist. We are seeking a skilled professional to spearhead the creation and implementation of credit risk models that inform critical lending strategies. By utilizing advanced statistical, econometric, and machine learning techniques on large datasets, you will play a vital role in minimizing credit losses and enhancing underwriting processes while generating valuable insights.
Your Profile: - You are an authority in credit risk, proficient in predictive modeling, causal inference, and experimental analysis.
- You can easily navigate between strategic planning and hands-on data science execution.
- You are passionate about leveraging analytics for impactful lending decisions and financial outcomes.
- You excel in cross-disciplinary collaboration and can convey complex technical ideas to non-technical stakeholders.
Your Responsibilities: - Create and refine credit risk models, deploying them into production with measurable improvements in performance metrics like AUC, ROC, and KS.
- Utilize statistical, machine learning, and causal inference methods to gain insights into customer behavior and predict future trends.
- Design, conduct, and analyze experiments (A/B tests, quasi-experiments) to direct product development and credit strategies.
- Establish and operationalize performance metrics frameworks and observability systems to monitor credit and product outcomes.
- Produce clear and impactful analyses and visualizations to guide cross-functional initiatives.
- Collaborate with engineers, product teams, DevOps, and data infrastructure specialists to deploy scalable, real-time modeling solutions.
- Lead the initiative to gather new data sources and optimize existing data pipelines for enhanced model features and insights.
- Engage with regulators and capital partners to ensure our models and risk management practices comply with regulations and capital adequacy standards.
Preferred Qualifications: - 6+ years of experience in credit risk modeling or analytics within consumer lending, ideally in a fast-paced fintech or neobank setting.
- 4+ years of applied data science experience demonstrating innovation and leadership.
- Expertise in SQL and strong proficiency in programming languages such as Python or R.
- Solid theoretical and practical understanding of statistics, machine learning, and experimental design.
- Proven track record of implementing credit models into production that drive significant business results.
- A Master's or Ph.D. in a quantitative discipline such as Computer Science, Mathematics, Engineering, or Economics from a reputable institution.