Position: Vice President, Data Science

Reporting to the Chief Technology Officer, the Vice President of Data Science is a senior technology leader responsible for defining and executing the organization’s data science and AI strategy. This leader transforms data into actionable insights, drives AI/ML innovation, and partners with operational leadership to influence product direction, operational excellence, and business outcomes. The VP of Data Science builds and leads high-performing teams, establishes scalable data science practices, and ensures models and insights are delivered ethically, reliably, and with measurable business impact.

Responsibilities

Strategic Leadership

• Define and own the enterprise data science vision, roadmap, and operating model aligned with company strategy.
• Translate business priorities into high-impact analytics, AI, and machine learning initiatives.
• Serve as an executive advisor on data-driven decision-making, AI adoption, and emerging technologies.
• Establish success metrics and ROI measurement for data science initiatives.

Organization Leadership

• Build, mentor, and scale a diverse, high-performing organization of data scientists and ML engineers.
• Set clear expectations, career paths, and performance standards.
• Foster a culture of curiosity, rigor, and collaboration.
• Create an environment where teams are empowered, accountable, and closely connected to the business.

• Oversee the design, development, deployment, and lifecycle management of predictive and prescriptive models.
• Ensure solutions are production-grade, integrated into applications and workflows, and supported by strong MLOps practices.
• Partner with engineering teams to embed models into clinical and operational systems.
• Establish governance, validation, and monitoring processes consistent with HIPAA and HITRUST requirements.

• Maintain a hands-on approach to data science and AI, including the ability to write, review, and guide code in modern data science and machine learning stacks.
• Stay close to the work by participating in model design, experimentation, and technical decision-making—especially for high-impact or clinically sensitive use cases.
• Provide technical leadership and mentorship by reviewing approaches, validating assumptions, and ensuring analytical rigor and model quality.
• Partner with data scientists and ML practitioners to unblock complex problems and set technical direction, without micromanaging execution.
• Serve as a credible technical voice with Data Engineering and Application Development teams on architecture, model deployment, and MLOps practices.
• Balance hands-on contribution with executive leadership, ensuring the organization benefits from both technical depth and strategic oversight.
• Lead by example in adopting best practices in machine learning, responsible AI, model explainability, and production readiness in a HIPAA-regulated environment consistent with the HITRUST framework.

Cross-Functional Partnerships

• Work closely with Data Engineering to ensure data quality, availability, and scalability.
• Collaborate with Application Development to embed analytics and models directly into workflows and products.
• Align on architecture, tooling, and MLOps practices that support both innovation and operational excellence.

What Success Looks Like

• Data science solutions are embedded into daily clinical and operational workflows, not siloed.
• Operation leaders and clinicians trust and rely on ML/AI to guide decisions.
• Models and insights are delivered quickly, responsibly, and with clear ROI.
• Data science is seen as a strategic partner, not a support function

Position Requirements

• Bachelor’s degree in a quantitative field (Computer Science, Statistics, Mathematics, Engineering, or similar).
• 10+ years of experience in data science, analytics, machine learning, or applied AI, with 3+ years in senior leadership roles.
• Proven track record of delivering data science solutions with clear business impact at scale.
• Deep expertise in statistical modeling, machine learning, and experimental design.
• Experience operationalizing models in production environments.
• Demonstrated success delivering ML or advanced analytics solutions in healthcare.
• Strong executive communication and stakeholder-management skills.

Benefits

• Comprehensive Benefits - Medical, dental, and vision insurance, employee assistance program, employer-paid and voluntary life insurance, disability insurance, plus health and flexible spending accounts
• Financial & Retirement Support – Competitive compensation, 401k with employer match, and financial wellness resources
• Time Off & Leave – Paid holidays, flexible vacation time/PSSL, and paid parental leave
• Wellness & Growth – Work life assistance resources, physical wellness perks, mental health support, employee referral program, and BenefitHub for employee discounts

About Monogram Health

Monogram Health is a leading multispecialty provider of in-home, evidence-based care for the most complex of patients who have multiple chronic conditions. Monogram Health takes a comprehensive and personalized approach to a person’s health, treating not only a disease, but all of the chronic conditions that are present - such as diabetes, hypertension, chronic kidney disease, heart failure, depression, COPD, and other metabolic disorders.

Monogram Health employs a robust clinical team, leveraging specialists across multiple disciplines including nephrology, cardiology, endocrinology, pulmonology, behavioral health, and palliative care to diagnose and treat health issues; review and prescribe medication; provide guidance, education, and counselling on a patient’s healthcare options; as well as assist with daily needs such as access to food, eating healthy, transportation, financial assistance, and more. Monogram Health is available 24 hours a day, 7 days a week, and on holidays, to support and treat patients in their home.

Monogram Health’s personalized and innovative treatment model is proven to dramatically improve patient outcomes and quality of life while reducing medical costs across the health care continuum.