Analyst within Credit Risk Management team identifying credit segmentation opportunities using statistical methods. Collaborating with teams to enhance credit decision process and policies.
About the role
- Develop, optimize, and maintain models for payment optimization, fraud detection, and LTV prediction
- Build robust end-to-end ML workflows, including data collection, feature engineering, model development, and evaluation
- Collaborate with Product and Engineering to deploy models in to production environments and monitor performance
- Design and analyze A/B tests and other experiments to assess model impact
- Analyze subscriber behavior, payment flows, and fraud patterns to generate actionable insights
- Partner with stakeholders to translate business needs into machine learning problems
Requirements
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field
- 3+ years of experience developing and deploying machine learning models in production
- Proficiency in SQL, Python (e.g. Pandas, NumPy, Scikit-learn, LightGBM)
- Experience with distributed computing tools such as Spark or PySpark
Benefits
- A bonus and/or long-term incentive units may be provided as part of the compensation package
- full range of medical, financial and/or other benefits, dependent on the level and position offered
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