Principal Data Scientist

Mariner Finance • Plano, TX

1d • $155,000 - $190,000 • Remote

About The Position

Mariner Finance is hiring a Principal Data Scientist to serve as a principal-level technical leader and advanced data science contributor. The role develops, evaluates, scales, and improves high-impact AI, machine learning, and predictive modeling solutions across business areas such as credit risk, collections, loss mitigation, decisioning, pricing, fraud, and customer strategy.

The Principal Data Scientist leads modeling initiatives from problem definition and experimentation through development, production deployment, monitoring, and optimization. The role applies advanced technical judgment to complex and ambiguous business problems and works with business and technical stakeholders to improve portfolio performance, profitability, risk management, and decision-making.

As an advanced technical resource in the Data Science function, this position provides technical direction, coaching, peer review, and escalation support. It also advances modeling practices, AI evaluation and measurement, reusable development patterns, and scalable approaches to responsible AI use. Work hours depend on the business hours of the time zone serviced, and the company may change the schedule to address business needs. The job is identified as remote.

Requirements

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field; additional applicable 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 across the full model lifecycle, from business problem definition through 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 use in model development, review, monitoring, documentation, and scalability.
• Experience designing and evaluating statistically rigorous experiments and AI/ML 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 and partnering with engineering, MLOps, or technology teams to integrate models into operational systems.
• 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, 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 select 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.
• Significant enterprise modeling experience in banking or financial services.
• Hands-on experience with reinforcement learning, causal inference, multi-armed bandits, transformer-based models, optimization, or similar advanced approaches, and judgment about when they are appropriate compared with 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 techniques and tools supporting model development, review, documentation, monitoring, optimization, and scale.
• Own the technical lifecycle of assigned modeling solutions, from problem formulation and data assessment through feature engineering, development, experimentation, validation, deployment, monitoring, recalibration, and enhancement.
• Translate complex or 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 techniques, including gradient boosting, tree-based models, causal inference, optimization, reinforcement learning, multi-armed bandits, and transformer-based models where appropriate.
• Design and lead rigorous experiments and evaluation approaches, including A/B testing, causal methods, AI/ML evaluation frameworks, and business-impact measurement.
• Develop and operationalize scalable modeling solutions using cloud and data platforms such as AWS and Databricks, partnering with engineering, MLOps, and technology teams on production integration.
• Monitor model performance and business outcomes; identify degradation, risks, or improvement opportunities and recommend recalibration, enhancement, replacement, or strategy changes.
• Design and implement evaluation frameworks measuring model quality, predictive performance, reliability, fairness where applicable, drift, stability, business impact, and fit for purpose.
• Partner with Risk, Loss Mitigation, Technology, Data, and other stakeholders to understand priorities, shape technical approaches, communicate findings, and support adoption of model-driven recommendations.
• Serve as a technical escalation point for complex modeling, experimentation, data, and 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.
• 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 for improvement.
• Conduct advanced portfolio analyses and recommend actions related to portfolio performance, risk, strategy, and improvement opportunities.
• 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 and refers candidates to the company benefits website for details.

What This Job Offers

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