Senior Director, Data Science & Machine Learning

Position Overview

The Senior Director, Data Science & Machine Learning provides strategic and technical leadership for Vibrant's Data Science, Machine Learning, and Artificial Intelligence function. Reporting to the Assistant Vice President, Data, this role is responsible for establishing and leading the organization's data science capabilities in support of the 988 Suicide & Crisis Lifeline, H2H (Here 2 Help), Community Programs, and enterprise initiatives.

Duties/Responsibilities

• Define and execute Vibrant's multi-year data science, machine learning, and AI strategy and roadmap in partnership with Technology leadership.
• Lead and grow the organization's data science and machine learning function, providing mentorship, technical leadership, and career development for team members.
• Translate organizational priorities into a structured applied research and delivery portfolio with measurable outcomes supporting 988 Lifeline, H2H, Community Programs, and enterprise initiatives.
• Advise executive leadership on emerging AI technologies, strategic opportunities, and build-versus-buy decisions.
• Represent Vibrant's data science and AI capabilities with executive stakeholders, federal partners, vendors, and external organizations.
• Establish and maintain technical standards for machine learning development, validation methodologies, code quality, documentation, reproducibility, and engineering best practices.
• Coach and mentor data scientists, analysts, and engineers through code reviews, technical guidance, and structured learning opportunities.
• Recruit, onboard, and retain high-performing data science and AI talent while defining organizational structure and future hiring strategy.
• Foster a culture grounded in scientific rigor, innovation, responsible AI, collaboration, and continuous improvement.
• Lead applied research initiatives that translate analytical findings into actionable recommendations and production-ready machine learning solutions.
• Design and oversee advanced quantitative research, program evaluation, and statistical modeling supporting organizational and federal reporting requirements.
• Develop and maintain outcome measurement frameworks that evaluate service quality, client outcomes, and operational performance.
• Oversee development, deployment, and monitoring of NLP and machine learning models supporting crisis services, including call summarization, sentiment analysis, quality assurance, risk detection, and routing optimization.
• Partner with engineering teams to ensure machine learning models are deployed within secure, HIPAA-compliant infrastructure and monitored throughout the model lifecycle.
• Ensure all production AI and machine learning systems meet governance, validation, documentation, audit, and regulatory requirements.
• Serve as the senior technical representative within Data Governance and Responsible AI governance forums, helping establish enterprise AI policies and standards.
• Collaborate with cross-functional technology, engineering, analytics, governance, and program leaders to ensure machine learning solutions align with operational priorities and clinical appropriateness.
• Support cooperative agreement deliverables, research reporting, and external program evaluation activities.

Required Skills/Abilities

• Executive-level expertise in statistical modeling, machine learning, natural language processing (NLP), and applied artificial intelligence.
• Deep technical knowledge of end-to-end machine learning lifecycle management, including model development, validation, deployment, monitoring, and optimization.
• Demonstrated experience leading and scaling high-performing data science, machine learning, or AI teams within complex organizations.
• Proven ability to establish technical standards, engineering best practices, and scientific rigor across data science initiatives.
• Strong experience translating applied research into production-ready machine learning systems that deliver measurable organizational impact.
• Experience designing quantitative research studies, evaluating complex analytical methods, and communicating research findings with appropriate scientific rigor.
• Knowledge of responsible AI frameworks, model governance, model risk management, fairness evaluation, and explainable AI principles.
• Experience working with highly regulated or sensitive data environments, including HIPAA, 42 CFR Part 2, or similar regulatory frameworks.
• Strong ability to partner with executive leadership, engineering, product, analytics, governance, and operational teams to deliver enterprise AI solutions.
• Demonstrated ability to recruit, mentor, develop, and retain technical talent while fostering a collaborative and psychologically safe team culture.
• Excellent written and verbal communication skills with the ability to communicate complex technical concepts to technical and non-technical audiences.
• Strong strategic thinking, decision-making, and organizational leadership capabilities.
• Demonstrated commitment to responsible AI, ethical machine learning, equity, transparency, and continuous improvement.
• Experience within healthcare, behavioral health, public health, nonprofit, or other mission-driven organizations strongly preferred.

Required Qualifications

• Bachelor's degree in Statistics, Computer Science, Data Science, Epidemiology, Public Health, or a related field required; Master's or Ph.D. strongly preferred.
• Minimum of 10+ years of progressive experience in applied data science and/or machine learning engineering.
• Minimum of 5 years of people leadership experience managing data scientists, machine learning engineers, or research teams.
• Proven experience delivering end-to-end production machine learning solutions, including deployment, monitoring, governance, and continuous model improvement.
• Strong technical proficiency with Python and modern machine learning frameworks such as scikit-learn, PyTorch, TensorFlow, Hugging Face, MLflow, Snowflake, dbt, or comparable technologies.
• Experience working in healthcare, behavioral health, crisis services, public health, or other federally regulated environments strongly preferred.
• Familiarity with HIPAA, 42 CFR Part 2, and governance requirements related to sensitive data.
• Experience with causal inference methodologies, advanced statistical analysis, or program evaluation preferred.
• Experience supporting federal grants, cooperative agreements, or government-funded programs is highly desirable.