Data Scientist – Credit
Job Description:
• Build high performance ML models that improve credit predictability and customer outcomes.
• Build end-to-end machine learning pipelines including data extraction, feature engineering, model building, performance evaluation, and implementation.
• Build end-to-end model monitoring pipelines to monitor model performance, including model stability, drift detection, and performance diagnostics.
• Develop and own reporting pipelines that support recurring risk reporting, monitoring, and ad-hoc analysis
• Explore rich data assets to uncover trends, identify key risk drivers, and generate insights.
• Define and monitor data quality metrics (completeness, accuracy, timeliness, consistency)
• Manage full modeling lifecycle: model design, data preparation and exploration, feature engineering, model development, evaluation, deployment, monitoring and documentation.
• Design experiments and challenger models to test new ideas and drive innovation.
• Contribute to audit-ready documentation and support model validation and governance processes.
• Leverage modern AI technology and tools to improve workflow, enhance modeling performance, establish and promote best practices.
• Work cross-functionally to ensure models are explainable, transparent, and aligned with business goals and regulatory expectations.
• Translate analytical findings into recommendations for Strategy, Pricing, and other business decisions.
• Build pipelines that are scalable, automated, and well-documented.
Requirements:
• A Master’s or PhD degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Data Science, Engineering, etc.).
• 1+ years of experience developing predictive models or performing advanced analytics.
• Strong understanding of statistical modeling and machine learning fundamentals.
• Proficiency in Python (e.g., pandas, numpy, scikit-learn) and SQL.
• Experience working with large datasets.
• Experience building end to end pipeline.
• Ability to clearly communicate analytical findings to both technical and non-technical audiences.
• Experience implementing data quality checks and monitoring.
• Demonstrated ownership, curiosity, and ability to deliver high-quality results.
Benefits:
• Pre-tax and post-tax retirement savings plans with a competitive company matching program
• Generous paid time-off plans including vacation, personal/sick time, paid short-term and long-term disability leaves, paid parental leave, and paid company holidays
• Multiple health care plans to choose from, including dental and vision options
• Flexible Spending Plans for Health Care, Dependent Care, and Health Reimbursement Accounts
• Company-paid benefits such as life insurance, wellness platforms, employee assistance programs, and Health Advocate programs
• Other great discounted benefits include identity theft protection, pet insurance, fitness center reimbursements, and many more!
• Build high performance ML models that improve credit predictability and customer outcomes.
• Build end-to-end machine learning pipelines including data extraction, feature engineering, model building, performance evaluation, and implementation.
• Build end-to-end model monitoring pipelines to monitor model performance, including model stability, drift detection, and performance diagnostics.
• Develop and own reporting pipelines that support recurring risk reporting, monitoring, and ad-hoc analysis
• Explore rich data assets to uncover trends, identify key risk drivers, and generate insights.
• Define and monitor data quality metrics (completeness, accuracy, timeliness, consistency)
• Manage full modeling lifecycle: model design, data preparation and exploration, feature engineering, model development, evaluation, deployment, monitoring and documentation.
• Design experiments and challenger models to test new ideas and drive innovation.
• Contribute to audit-ready documentation and support model validation and governance processes.
• Leverage modern AI technology and tools to improve workflow, enhance modeling performance, establish and promote best practices.
• Work cross-functionally to ensure models are explainable, transparent, and aligned with business goals and regulatory expectations.
• Translate analytical findings into recommendations for Strategy, Pricing, and other business decisions.
• Build pipelines that are scalable, automated, and well-documented.
Requirements:
• A Master’s or PhD degree in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Data Science, Engineering, etc.).
• 1+ years of experience developing predictive models or performing advanced analytics.
• Strong understanding of statistical modeling and machine learning fundamentals.
• Proficiency in Python (e.g., pandas, numpy, scikit-learn) and SQL.
• Experience working with large datasets.
• Experience building end to end pipeline.
• Ability to clearly communicate analytical findings to both technical and non-technical audiences.
• Experience implementing data quality checks and monitoring.
• Demonstrated ownership, curiosity, and ability to deliver high-quality results.
Benefits:
• Pre-tax and post-tax retirement savings plans with a competitive company matching program
• Generous paid time-off plans including vacation, personal/sick time, paid short-term and long-term disability leaves, paid parental leave, and paid company holidays
• Multiple health care plans to choose from, including dental and vision options
• Flexible Spending Plans for Health Care, Dependent Care, and Health Reimbursement Accounts
• Company-paid benefits such as life insurance, wellness platforms, employee assistance programs, and Health Advocate programs
• Other great discounted benefits include identity theft protection, pet insurance, fitness center reimbursements, and many more!