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Principal Data Scientist

Mariner Finance Nottingham, MD

Nottingham, MD

Today

$155k - $195k/yr Full time AI Artificial Intelligence Cloud Platform

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Job Description

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.

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