Director, Data Science

As a Senior Data Science Leader at our company, you will be responsible for owning the strategy, roadmap, and delivery of machine-learning and statistical models that drive our consumer lending business. Your role will involve leading the development of credit risk models for underwriting and portfolio risk decisioning, as well as overseeing models related to fraud risk, marketing response and propensity, customer lifecycle management, collections prioritization, account management, and pricing, line, and offer optimization. You will play a crucial role in setting analytical standards, building validation-ready models, mentoring a team of data scientists and ML practitioners, and collaborating with other business leaders. Key Responsibilities: - Own the model development strategy and roadmap for consumer lending decisioning, covering various use cases such as credit risk underwriting, fraud, marketing, customer lifecycle, collections, and pricing optimization. - Lead, hire, coach, and develop a team of data scientists and ML practitioners, setting technical direction, performance expectations, and career paths. - Manage the end-to-end model lifecycle, including problem framing, data engineering, model development, deployment, monitoring, recalibration, and retirement. - Drive feature engineering and evaluate alternative data sources to enhance model performance and compliance. - Establish champion/challenger frameworks for experimentation and backtesting to measure model impact. - Monitor model performance, drift, and stability, and ensure compliance with regulatory expectations. - Collaborate with various teams to productionize models and ensure reliable real-time and batch scoring. - Translate model outputs into actionable business strategies and communicate results to executives and stakeholders. - Set and maintain analytical standards, tooling, and reproducibility practices across the data science function. Qualifications: - 10+ years of experience in data science, statistical modeling, or credit risk analytics. - 3+ years of experience in leading and developing data science or ML teams. - Expertise in building credit risk and portfolio decisioning models for consumer lending. - Proficiency in Python, SQL, and modern ML and statistical methods. - Experience across the full model lifecycle and familiarity with regulatory requirements in financial services. - Excellent communication skills and the ability to explain complex modeling concepts clearly. This is a challenging yet rewarding opportunity for a seasoned data science professional to make a significant impact on our consumer lending business. As a Senior Data Science Leader at our company, you will be responsible for owning the strategy, roadmap, and delivery of machine-learning and statistical models that drive our consumer lending business. Your role will involve leading the development of credit risk models for underwriting and portfolio risk decisioning, as well as overseeing models related to fraud risk, marketing response and propensity, customer lifecycle management, collections prioritization, account management, and pricing, line, and offer optimization. You will play a crucial role in setting analytical standards, building validation-ready models, mentoring a team of data scientists and ML practitioners, and collaborating with other business leaders. Key Responsibilities: - Own the model development strategy and roadmap for consumer lending decisioning, covering various use cases such as credit risk underwriting, fraud, marketing, customer lifecycle, collections, and pricing optimization. - Lead, hire, coach, and develop a team of data scientists and ML practitioners, setting technical direction, performance expectations, and career paths. - Manage the end-to-end model lifecycle, including problem framing, data engineering, model development, deployment, monitoring, recalibration, and retirement. - Drive feature engineering and evaluate alternative data sources to enhance model performance and compliance. - Establish champion/challenger frameworks for experimentation and backtesting to measure model impact. - Monitor model performance, drift, and stability, and ensure compliance with regulatory expectations. - Collaborate with various teams to productionize models and ensure reliable real-time and batch scoring. - Translate model outputs into actionable business strategies and communicate results to executives and stakeholders. - Set and maintain analytical standards, tooling, and reproducibility practices across the data science function. Qualifications: - 10+ years of experience in data science, statistical modeling, or credit risk analytics. - 3+ years of experience in leading and developing data science or ML teams. - Expertise in building credit risk and portfolio decisioning models for consumer lending. - Proficiency in Python, SQL, and modern ML and statistical methods. - Experience across the full model lifecy