Principal Data Scientist

Base Salary Range

USD $155,000.00 - USD $190,000.00 /Yr.

Job Details

In this role, you will...

Serve as a principal-level technical leader and advanced data science contributor responsible for developing, evaluating, scaling, and improving complex, high-impact AI, machine learning, and predictive modeling solutions across priority business areas, including credit risk, collections, loss mitigation, decisioning, pricing, fraud, and customer strategy.

Lead end-to-end data science initiatives from problem definition and experimentation through model development, production deployment, monitoring, and ongoing optimization. Apply advanced technical judgment to determine appropriate modeling approaches for complex and ambiguous business problems and partner with business and technical stakeholders to deliver measurable improvements in portfolio performance, profitability, risk management, and decision-making.

Serve as an advanced technical resource within the Data Science function by providing technical direction, coaching, peer review, and escalation support. Raise the technical standard of the team by advancing modeling practices, AI evaluation and measurement methods, reusable development patterns, and scalable approaches for applying AI responsibly across model development and business decisioning.

Responsibilities and Duties:

• Lead the development, evaluation, and operationalization of complex predictive, machine learning, and AI models, including the use of AI-enabled techniques and tools to accelerate model development, model review, documentation, monitoring, optimization, and scale across high-impact business use cases.
• Own the end-to-end technical lifecycle of assigned modeling solutions, including problem formulation, data assessment, feature engineering, model development, experimentation, validation, deployment, performance monitoring, recalibration, and ongoing enhancement.
• Translate complex or ambiguous business challenges into analytical and modeling strategies, evaluating alternative approaches, and recommending solutions based on expected business impact, technical feasibility, risk, and scalability.
• Apply advanced statistical, machine learning, and artificial intelligence techniques to solve complex business problems, including traditional predictive modeling methods such as gradient boosting and tree-based models, as well as causal inference, optimization, reinforcement learning, multi-armed bandits, transformer-based models, and other appropriate methods.
• Design and lead statistically rigorous experiments and evaluation approaches, including A/B testing, causal methods, AI/ML evaluation frameworks, and business-impact measurement to assess model effectiveness, reliability, fairness where applicable, and operational value.
• Develop and operationalize scalable modeling solutions using cloud and data platforms such as AWS and Databricks, partnering with engineering, MLOps, and technology teams to support effective integration into production environments.
• Monitor model performance and business outcomes after implementation; identify degradation, emerging risks, or opportunities for improvement and recommend appropriate recalibration, enhancement, replacement, or strategy changes.
• Design and implement evaluation frameworks and measurement approaches for AI and machine learning solutions, including model quality, predictive performance, reliability, drift, stability, business impact, fit-for-purpose assessment, and other relevant effectiveness measures.
• Partner with Risk, Loss Mitigation, Technology, Data, and other business stakeholders to understand business priorities, shape technical approaches, communicate findings, and support adoption of model-driven recommendations and decisioning.
• Serve as a technical escalation point for complex modeling, experimentation, data, or implementation challenges and provide guidance on appropriate analytical and technical approaches.
• Review and challenge modeling methodologies, technical designs, assumptions, and analytical conclusions to support quality, consistency, scalability, and sound data science practices.
• Provide technical guidance, coaching, and knowledge sharing to data scientists and other team members, helping strengthen technical capabilities and consistent application of data science practices.
• Contribute to the development and advancement of modeling standards, reusable analytical approaches, methodologies, documentation practices, and technical patterns that improve the effectiveness and scalability of the Data Science function.
• Partner with senior leaders and other applicable stakeholders to support appropriate model governance, documentation, validation, monitoring, controls, and lifecycle requirements.
• Evaluate emerging modeling techniques, technologies, tools, and data sources and recommend opportunities to improve predictive performance, experimentation capabilities, operational efficiency, or business outcomes.
• Conduct advanced portfolio analyses and provide recommendations to business and technical stakeholders regarding portfolio performance, risk, strategy, and opportunities for improvement.
• Communicate complex analytical concepts, model performance, tradeoffs, risks, and recommendations clearly to technical and non-technical audiences.
• May perform additional functions depending on business needs.

Required Qualifications:

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field; additional applicable experience may be substituted for a bachelor's degree.
• Minimum of seven (7) years of progressive data science, statistical modeling, or machine learning experience, including at least five (5) years of relevant experience developing and operationalizing predictive or machine learning models.
• Demonstrated experience leading complex modeling initiatives through the full model lifecycle, from business problem definition and model development through production deployment, monitoring, and ongoing optimization.
• Advanced proficiency in Python and SQL and experience with commonly used machine learning, statistical modeling, and data science frameworks and libraries.
• Advanced experience developing and deploying modeling solutions within cloud-based environments, including AWS.
• Experience working with modern data and analytics platforms such as Databricks or comparable technologies.
• Advanced knowledge of predictive modeling, machine learning, artificial intelligence, statistical analysis, experimentation, model evaluation, performance measurement, and practical use of AI to improve model development, review, monitoring, documentation, and scalability.
• Demonstrated experience designing and evaluating statistically rigorous experiments and AI/ML evaluation approaches, including measurement frameworks that translate model quality, reliability, drift, and business-impact results into actionable business recommendations.
• Experience developing, evaluating, and scaling production AI and machine learning solutions and partnering with engineering, MLOps, or technology teams to integrate models into operational systems, with demonstrated ability to use AI-enabled tools or methods to improve the speed, quality, repeatability, or scalability of data science delivery.
• Strong understanding of model lifecycle management, model performance monitoring, documentation, and model risk considerations. Demonstrated experience developing or applying AI evaluation and measurement frameworks for models or AI-enabled systems, including defining success criteria, testing failure modes, assessing production readiness, and communicating limitations and tradeoffs.
• Demonstrated ability to independently solve complex and ambiguous analytical problems and determine appropriate modeling approaches with limited direction.
• Experience providing technical guidance, coaching, peer review, or mentorship to other data scientists or technical team members. Ability to serve as a senior technical thought partner to leadership by shaping modeling strategy, challenging assumptions, improving standards, and helping the Data Science function scale its AI and machine learning capabilities.
• Strong stakeholder management and communication skills, including the ability to communicate complex technical concepts, risks, tradeoffs, and recommendations to technical and non-technical audiences.
• Strong analytical, problem-solving, organizational, and decision-making skills.
• Ability to manage multiple complex initiatives and priorities while working independently and collaboratively across functions.

Preferred Qualifications:

• Advanced degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.
• Experience within consumer lending, particularly supporting credit risk, collections, loss mitigation, decisioning, loss forecasting, optimization, or related modeling use cases.
• Alternatively, significant enterprise modeling experience within a banking or financial services environment.
• Hands-on experience with advanced modeling approaches such as reinforcement learning, causal inference, multi-armed bandits, transformer-based models, optimization techniques, or similar methods, including sound judgment about when these approaches are appropriate versus simpler traditional machine learning methods.
• Experience working within established model risk management or model governance frameworks.

Hours of Work:

Full-time. Work hours will depend on the business hours of the time zone serviced. To the extent permitted by law, the Company may, in its sole discretion, change the work schedule to address business needs.

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit for extended periods; reach with hands and arms; and talk or hear. The employee is occasionally required to move about. The employee must occasionally lift and/or move up to twenty (20) pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus.

This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Duties, responsibilities, and activities may change or new ones may be assigned at any time or without notice.

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