Principal Data Scientist

Mariner Finance • Nottingham, MD

1d • $155,000 - $195,000 • Remote

About The Position

Serve as a principal-level technical leader and advanced data science contributor developing, evaluating, scaling, and improving high-impact AI, machine learning, and predictive modeling solutions across areas such as credit risk, collections, loss mitigation, decisioning, pricing, fraud, and customer strategy. Lead initiatives from problem definition and experimentation through model development, production deployment, monitoring, and optimization. Apply advanced technical judgment to complex business problems and partner with business and technical stakeholders to improve portfolio performance, profitability, risk management, and decision-making. Provide technical direction, coaching, peer review, and escalation support, and advance modeling practices, AI evaluation methods, reusable development patterns, and responsible, scalable AI use.

The role partners with Risk, Loss Mitigation, Technology, Data, engineering, MLOps, and other stakeholders to deliver production-ready modeling solutions and support their adoption. It also contributes to model governance, documentation, validation, monitoring, controls, and lifecycle requirements. Work hours depend on the business hours of the time zone serviced; the Company may change the schedule to address business needs. The position is full-time.

Requirements

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field; applicable additional experience may substitute for a bachelor’s degree.
• At least seven years of progressive data science, statistical modeling, or machine learning experience, including at least five years developing and operationalizing predictive or machine learning models.
• Experience leading complex modeling initiatives through the full lifecycle, from business problem definition and development to production deployment, monitoring, and 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 in cloud environments, including AWS.
• Experience with modern data and analytics platforms such as Databricks or comparable technologies.
• Advanced knowledge of predictive modeling, machine learning, AI, statistical analysis, experimentation, model evaluation, performance measurement, and practical AI applications for improving data science delivery.
• Experience designing and evaluating statistically rigorous experiments and AI/ML evaluation approaches, and translating model quality, reliability, drift, and business-impact results into actionable recommendations.
• Experience developing, evaluating, and scaling production AI and machine learning solutions; partnering with engineering, MLOps, or technology teams to integrate models into operational systems; and using AI-enabled tools or methods to improve delivery speed, quality, repeatability, or scalability.
• Strong understanding of model lifecycle management, model performance monitoring, documentation, and model risk considerations.
• Experience developing or applying AI evaluation and measurement frameworks, including defining success criteria, testing failure modes, assessing production readiness, and communicating limitations and tradeoffs.
• Ability to independently solve complex, ambiguous analytical problems and determine appropriate modeling approaches with limited direction.
• Experience providing technical guidance, coaching, peer review, or mentorship to 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 scale AI and machine learning capabilities.
• Strong stakeholder management and communication skills, including communicating 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 independently and collaboratively across functions.

Nice To Haves

• Advanced degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.
• Experience in consumer lending, particularly credit risk, collections, loss mitigation, decisioning, loss forecasting, optimization, or related modeling use cases.
• Significant enterprise modeling experience within banking or financial services.
• Hands-on experience with reinforcement learning, causal inference, multi-armed bandits, transformer-based models, optimization, or similar advanced approaches, with sound judgment about when to use them instead of traditional machine learning methods.
• Experience working within established model risk management or model governance frameworks.

Responsibilities

• Lead development, evaluation, and operationalization of complex predictive, machine learning, and AI models, including AI-enabled methods and tools that improve development, review, documentation, monitoring, optimization, and scale.
• Own the technical lifecycle of assigned modeling solutions, including problem formulation, data assessment, feature engineering, development, experimentation, validation, deployment, monitoring, recalibration, and enhancement.
• Translate ambiguous business challenges into analytical strategies, assess alternative approaches, and recommend solutions based on business impact, feasibility, risk, and scalability.
• Apply statistical, machine learning, and AI methods, including gradient boosting, tree-based models, causal inference, optimization, reinforcement learning, multi-armed bandits, and transformer-based models as appropriate.
• Design and lead rigorous experiments and evaluation approaches, including A/B testing, causal methods, AI/ML evaluation frameworks, and business-impact measurement.
• Develop scalable modeling solutions using cloud and data platforms such as AWS and Databricks, partnering with engineering, MLOps, and technology teams to integrate them into production.
• Monitor model performance and business outcomes, identify degradation, risks, and improvement opportunities, and recommend recalibration, enhancement, replacement, or strategy changes.
• Design and implement AI/ML evaluation frameworks covering quality, predictive performance, reliability, drift, stability, business impact, and fit-for-purpose assessment.
• Partner with Risk, Loss Mitigation, Technology, Data, and other stakeholders to understand priorities, shape technical approaches, communicate findings, and support model-driven recommendations and decisioning.
• Serve as a technical escalation point for complex modeling, experimentation, data, or implementation challenges.
• Review and challenge modeling methodologies, technical designs, assumptions, and analytical conclusions to support quality, consistency, scalability, and sound practices.
• Provide technical guidance, coaching, peer review, and knowledge sharing to data scientists and other team members.
• Advance modeling standards, reusable analytical approaches, methodologies, documentation practices, and technical patterns for the Data Science function.
• Partner with senior leaders and stakeholders on model governance, documentation, validation, monitoring, controls, and lifecycle requirements.
• Evaluate emerging modeling techniques, technologies, tools, and data sources and recommend opportunities to improve performance, experimentation, efficiency, or business outcomes.
• Conduct advanced portfolio analyses and recommend improvements concerning portfolio performance, risk, and strategy.
• Communicate analytical concepts, model performance, tradeoffs, risks, and recommendations to technical and non-technical audiences.
• Perform additional functions depending on business needs.

Benefits

• The posting states that all full-time employees receive a generous benefits package in addition to monetary compensation and refers applicants to the company benefits webpage for details.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal