VP of Data and AI

Overview

National Debt Relief (NDR) is seeking a highly technical and hands‑on Vice President of Data and ML/AI to lead the strategy, execution, and evolution of our enterprise data and artificial intelligence ecosystem. This role will oversee the enterprise data warehouse, data engineering, analytics platforms, governance, machine learning operations, and applied AI. The VP will ensure that NDR’s data foundation, data science and AI capabilities are reliable, scalable, and business‑aligned, while fostering innovation and enabling the company to harness data as a competitive advantage.

The VP of Data and ML/AI will partner closely with executive leadership to deliver governed, trusted data products and applied AI solutions that drive growth, operational efficiency, and client success. This role requires deep expertise in Snowflake, data engineering, and MLOps workflows, along with strong business acumen and the ability to align technical execution with enterprise priorities.

• Define and lead NDR’s enterprise data and AI/ML strategy, spanning data engineering, analytics enablement, governance, machine learning operations, data science, and applied AI.
• Ensure best‑in‑class data infrastructure through Snowflake and modern data stack tools, delivering scalable, secure, and high‑quality enterprise data.
• Own enterprise data governance, access, and security, including role‑based controls, data quality frameworks, and compliance with privacy regulations.
• Drive adoption and enablement of analytics platforms and curated self‑service data models for data users across the enterprise.
• Establish and maintain MLOps practices for deploying, monitoring, retraining, and governing machine learning models in batch and API contexts.
• Guide the applied AI and data science functions to ensure initiatives are designed, deployed, and maintained with measurable business impact.
• Partner with analytics leaders and business stakeholders to align data, AI, and platform strategies with enterprise goals.
• Define and standardize the enterprise data model, metrics, and documentation to ensure clarity, reusability, and alignment across functions.
• Lead and develop a multi‑level technical organization, cultivating strong engineering and AI expertise while advancing professional growth.
• Champion a culture of data‑driven decision‑making, continuous improvement, and technical excellence.
• Stay ahead of trends in cloud data engineering, MLOps, and AI adoption, and guide NDR in responsibly scaling AI‑powered capabilities.

Education/

Experience:

• Bachelor’s degree in Computer Science, Data Engineering, Statistics, or a related field required;
  Master’s degree or MBA preferred.
• 15+ years of experience in data engineering, analytics platforms, or applied data science, with at least 6 years in senior leadership roles.
• Proven expertise building and managing enterprise‑scale data ecosystems in Snowflake.
• Experience leading functions across data engineering, analytics enablement, governance, and ML/AI.
• Demonstrated success deploying and scaling MLOps frameworks and managing ML/AI solutions in production environments.
• Prior experience in financial services, fintech, or consumer‑facing industries strongly preferred.

Required Skills/Abilities:

• Hands‑on expertise in Snowflake, SQL, Python, and modern data stack tools (dbt, Fivetran, Dagster).
• Deep knowledge of enterprise data architecture, pipelines, and governance practices.
• Strong understanding of machine learning concepts, model evaluation, and lifecycle management, with ability to guide deployments even if not building models directly.
• Proven track record implementing MLOps for scalable, monitored, and governed model deployments.
• Familiarity with BI and analytics platforms (Sigma, Tableau, Power BI) and integration with enterprise data pipelines.
• Strong executive presence with ability to engage both technical and business leaders, including at the board level.
• Excellent leadership and people management skills, with demonstrated success developing multi‑level technical organizations.
• Ability to translate technical depth into business outcomes, aligning data and AI execution with strategic…