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.

• 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.

• Bachelor’s degree in Data Science, Statistics, Mathematics,…