Principal Data Scientist

Base Salary Range USD $155,000.00 - USD $195,000.00 /Yr. Join Mariner Finance! Since 1927, the Mariner Finance family of companies has provided customers with creative, flexible, and convenient lending options. Headquartered in Baltimore, Mariner Finance operates coast-to-coast with physical locations in over half the states. With a growing number of employees, superior customer service remains the cornerstone of our business, and we pride ourselves in delivering a variety of loans with an enhanced focus on exceptional service. We work with customers to find options that are beneficial to their specific needs, which is why we are recognized by our customers as one of the community’s consumer finance companies of choice. Benefits: For information regarding our benefits, please visit: https://www.marinerfinance.com/careers/benefits/  All full time employees are provided with a generous benefits package in addition to their monetary compensation. Learn more about it today. 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 prov