Principal Data Scientist

Mariner Finance•Plano, TX

1d•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 complex AI, machine learning, and predictive modeling solutions across business areas including credit risk, collections, loss mitigation, decisioning, pricing, fraud, and customer strategy. The position leads end-to-end data science initiatives, from problem definition and experimentation through development, production deployment, monitoring, and ongoing optimization, with the goal of improving portfolio performance, profitability, risk management, and decision-making. The Principal Data Scientist provides technical direction, coaching, peer review, and escalation support within the Data Science function. The role advances modeling practices, AI evaluation and measurement, reusable development patterns, and responsible, scalable AI applications. It partners with business and technical stakeholders—including Risk, Loss Mitigation, Technology, Data, engineering, and MLOps teams—to develop and operationalize solutions, communicate findings, support model-driven decisions, and maintain appropriate governance and lifecycle practices. The role is full-time. Work hours depend on the business hours of the time zone serviced, and the company may change the schedule to address business needs. The position is listed as remote. Physical demands include frequently sitting for extended periods, reaching with hands and arms, and talking or hearing; occasionally moving about and lifting or moving up to 20 pounds; and having close vision and the ability to adjust focus.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field; applicable additional experience may be substituted 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 model development through production deployment, monitoring, and optimization.
Advanced proficiency in Python and SQL, with experience using 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 working 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 in data science delivery.
Experience designing and evaluating statistically rigorous experiments and AI/ML evaluation approaches, including measurement frameworks that translate model quality, reliability, drift, and business impact 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 other 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, especially credit risk, collections, loss mitigation, decisioning, loss forecasting, optimization, or related modeling use cases.
Alternatively, significant enterprise modeling experience in banking or financial services.
Hands-on experience with reinforcement learning, causal inference, multi-armed bandits, transformer-based models, optimization techniques, or similar approaches, with sound judgment about when they are appropriate versus traditional machine learning methods.
Experience working within established model risk management or model governance frameworks.

Responsibilities

Lead the development, evaluation, and operationalization of complex predictive, machine learning, and AI models, using AI-enabled techniques and tools where appropriate to improve development, review, documentation, monitoring, optimization, and scale.
Own the end-to-end technical lifecycle of assigned modeling solutions, including problem formulation, data assessment, feature engineering, development, experimentation, validation, deployment, performance monitoring, recalibration, and enhancement.
Translate complex or ambiguous business challenges into analytical and modeling strategies; evaluate alternative approaches and recommend solutions based on business impact, technical feasibility, risk, and scalability.
Apply advanced statistical, machine learning, and AI techniques, including traditional predictive modeling and, where appropriate, causal inference, optimization, reinforcement learning, multi-armed bandits, and transformer-based models.
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, and improvement opportunities, and recommend recalibration, enhancement, replacement, or strategy changes.
Design and implement evaluation frameworks for model 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 adoption of model-driven recommendations and decisioning.
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 data science 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 relevant 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 relating to 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.