Principal Data Scientist

Principal Data Scientist role at Mariner Finance in Nottingham, MD (onsite) focused on end-to-end AI/ML and predictive modeling across priority business areas.

Responsibilities

• Lead development, evaluation, and operationalization of complex predictive, machine learning, and AI models, including AI-enabled techniques and tools to accelerate model development, review, documentation, monitoring, optimization, and scaling.
• Own the full technical lifecycle for modeling solutions: problem formulation, data assessment, feature engineering, model development, experimentation, validation, deployment, performance monitoring, recalibration, and ongoing enhancements.
• Convert complex or unclear business problems into analytical and modeling strategies; evaluate alternatives and recommend solutions based on expected business impact, technical feasibility, risk, and scalability.
• Apply advanced statistical, machine learning, and AI methods to business problems, including gradient boosting and tree-based models, plus causal inference, optimization, reinforcement learning, multi-armed bandits, transformer-based models, and other appropriate approaches.
• Design and lead statistically rigorous experimentation and evaluation, including A/B testing, causal methods, AI/ML evaluation frameworks, and measurement of business impact; assess effectiveness, reliability, fairness where applicable, and operational value.
• Build and operationalize scalable modeling solutions using cloud and data platforms such as AWS and Databricks; partner with engineering, MLOps, and technology teams for production integration.
• Monitor post-deployment model performance and business outcomes; detect degradation, emerging risks, or improvement opportunities; recommend recalibration, enhancement, replacement, or strategy changes.
• Create evaluation frameworks and measurement approaches for AI/ML solutions, including assessment of model quality, predictive performance, reliability, drift, stability, business impact, fit-for-purpose, and related effectiveness measures.
• Partner with Risk, Loss Mitigation, Technology, Data, and other stakeholders to align on business priorities, communicate insights, and support adoption of model-driven decisioning.
• Act as a technical escalation point for complex modeling, experimentation, data, or implementation challenges; provide guidance on 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 to strengthen technical capabilities and consistent data science practices.
• Contribute to modeling standards, reusable analytical approaches, methodologies, documentation practices, and technical patterns that improve scalability and effectiveness.
• Partner with senior leaders and applicable stakeholders to support model governance, documentation, validation, monitoring, controls, and lifecycle requirements.
• Evaluate emerging modeling techniques, technologies, tools, and data sources; recommend opportunities to improve predictive performance, experimentation capabilities, operational efficiency, and business outcomes.
• Conduct advanced portfolio analyses and provide recommendations to business and technical stakeholders on portfolio performance, risk, strategy, and improvement opportunities.
• Communicate complex analytical concepts, model performance, tradeoffs, risks, and recommendations to both technical and non-technical audiences.
• May perform additional functions depending on business needs.

Requirements

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field; additional applicable experience may be substituted.
• Minimum 7 years of progressive data science, statistical modeling, or machine learning experience, including at least 5 years developing and operationalizing predictive or machine learning models.
• Demonstrated experience leading complex modeling initiatives across the full lifecycle: business problem definition, development, production deployment, monitoring, and ongoing optimization.
• Advanced proficiency in Python and SQL; experience with common 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/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 across development, review, monitoring, documentation, and scalability.
• Proven ability designing and evaluating statistically rigorous experiments and AI/ML evaluation approaches, including measurement frameworks that connect quality, reliability, drift, and business impact to actionable recommendations.
• Experience developing, evaluating, and scaling production AI/ML solutions; partnering with engineering, MLOps, or technology teams to integrate models into operational systems; demonstrated ability to use AI-enabled tools or methods to improve speed, quality, repeatability, or scalability of delivery.
• Strong understanding of model lifecycle management, model performance monitoring, documentation, and model risk; experience defining success criteria, testing failure modes, assessing production readiness, and communicating limitations and tradeoffs via AI evaluation and measurement frameworks.
• 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; ability to serve as a senior technical thought partner by shaping strategy, challenging assumptions, improving standards, and scaling Data Science AI/ML capabilities.
• Strong stakeholder management and communication skills, including explaining 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.

Technologies

• Python, SQL
• AWS, Databricks
• Gradient boosting, tree-based models
• A/B testing
• Reinforcement learning, multi-armed bandits
• Transformer-based models
• Causal inference
• MLOps

Benefits

• For information regarding benefits, visit: https://www.marinerfinance.com/careers/benefits/
• All full time employees are provided with a generous benefits package in addition to monetary compensation.

Hours of Work

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

Physical Demands

• Frequently required to sit for extended periods; reach with hands and arms; and talk or hear.
• Occasionally required to move about.
• Occasionally lift and/or move up to twenty (20) pounds.
• Specific vision abilities required: close vision and ability to adjust focus.

EEO

• Mariner Finance is an Equal Opportunity Employer and does not discriminate based on protected characteristics as listed in the EEO statement.
• Reasonable accommodations will be made for employees with disabilities, absent undue hardship, to perform essential job duties if requested.
• Drug/Alcohol/Smoke-free workplace.