Director of Data Science

Overview

National Debt Relief (NDR) is seeking a highly technical and hands-on Director of Data Science to lead a growing team of machine learning practitioners and data science product partners within the Data & AI organization. As a key member of the Data & AI leadership team, the Director of Data Science will oversee the end-to-end development, deployment, and monitoring of predictive and prescriptive machine learning models across core business functions. This role is ideal for a rising leader with strong prior experience in both individual contributor and team management roles—someone who is looking for their first Director title but is already fluent in production-grade ML deployment and the strategic framing of data science work.

You will lead a high-performing team focused on driving value through supervised and unsupervised learning models, partnering with stakeholders in product, engineering, and operations to prioritize high-ROI use cases. You will be responsible for setting vision and roadmap for data science initiatives, owning model performance and business impact, and enabling operationalized ML capabilities via batch inference and real-time APIs. You will also work closely with the Director of Data Engineering and the ML/AI Ops Engineer to ensure all deployments are robust, scalable, and aligned with best practices in model governance and MLOps.

This is a highly visible role requiring strong technical depth, business acumen, and executive communication skills. The ideal candidate combines deep fluency in Python and SQL with strong leadership instincts, and a passion for using data science to create measurable business value.

Responsibilities

• Lead, mentor, and grow a team of data scientists and a data science product manager focused on delivering production-ready ML models in support of NDR’s mission.
• Guide discovery, scoping, and business case development for data science opportunities across the company, ensuring alignment with strategic priorities and ROI potential.
• Personally contribute to solution design, data preparation, model development, and deployment architecture for key ML initiatives.
• Own the full model lifecycle: from exploration and prototyping to production deployment, performance monitoring, and iterative refinement.
• Partner with data engineering and ML/AI Ops teams to ensure scalable, monitored deployments via Snowflake, DataRobot, and real-time APIs.
• Drive rigorous standards for model validation, performance evaluation, documentation, and reproducibility.
• Establish clear roadmaps and delivery timelines in collaboration with your product manager, ensuring transparency and alignment with business partners.
• Present complex analytical concepts and model results to executive stakeholders in a clear, impactful manner.
• Promote best practices in ethical AI use, model explainability, and responsible ML across all stages of delivery.
• Collaborate closely with the rest of the Data & AI leadership team to ensure organizational cohesion, reuse of curated data, and unified enablement of AI-powered capabilities.

Qualifications

Education/Experience:

• Bachelor’s degree required. Bachelor's or Advanced degree in Data Science, Business Analytics, Computer Science, or related field strongly preferred.
• 10 years of experience in data science or applied machine learning roles, with 2 or more years in a formal team leadership or player-coach capacity.
• Proven track record of deploying production-grade machine learning models in a business environment, including batch and real-time use cases.
• Strong understanding of supervised and unsupervised learning algorithms and use cases.
• Hands-on experience with model deployment workflows, including performance monitoring, drift detection, and ongoing retraining.
• Demonstrated success partnering with product, engineering, and business stakeholders to deliver measurable impact from data science initiatives.
• Experience building or guiding models using tools such as Snowflake, DataRobot, or comparable platforms.
• Experience with Snowflake-native ML workflows or MLOps pipelines integrated with Snowflake.
• Understanding of model governance, explainability, and responsible AI practices.
• Experience in financial services, lending, or other high-volume, consumer-facing environments.

Required Skills/Abilities:

• Expert-level proficiency in Python and SQL, including experience with feature engineering, model pipelines, and exploratory analysis.
• Excellent communication skills with the ability to explain technical concepts to non-technical audiences, including executive stakeholders.
• Strong project management and prioritization skills; able t