Head of Data Science

About the role

The Head of Data Science will own the end to end lifecycle of data science strategy and execution for Abridge's healthcare platform. They will establish the vision for a world class data organization and translate complex clinical problems into scalable analytical solutions. This role requires high agency to drive data democratization in the age of AI while ensuring rigorous scientific standards. The hire will build and manage a growing U.S. based team, setting the bar for mentorship, authorship, and impact. They will act as the central data thought partner for the executive team and ensure that data drives every major product and business decision.

Key facts

What you'll do

• Build and manage a world-class data science team, providing guidance, mentorship, and high standards while scaling the U.S. based function.
• Foster a high impact, collaborative team culture that emphasizes agency, clarity of thinking, and pride in authorship.
• Drive product strategy through data-driven insights across a growing portfolio, including user behavior analysis, deep-dives into product performance metrics, and causal inference experimentation.
• Partner with product, strategy, and research teams to develop sophisticated ROI frameworks for customers, ingesting real-time data and demonstrating measurable impact.
• Collaborate with a world-class research team on model and framework evaluation, shaping model evaluation standards, production performance monitoring, and clinically meaningful quality metrics.
• Communicate data strategy, complex analyses, and key insights effectively to cross-functional partners, including executives and commercial stakeholders.
• Structure the company's data strategy in close collaboration with the Data Engineering team, identifying gaps in internal and external data sources and optimizing data ingestion and structuring.
• Make critical technical infrastructure decisions for the data organization, including tooling choices, build vs buy tradeoffs for analytics platforms, and setting technical standards.
• Build the data science organization of the future by incorporating current and emerging best practices to leverage AI for faster data ingestion and insight generation.
• Define and own the data science roadmap, aligning priorities with clinical, product, and business objectives while managing competing demands in a fast growth environment.
• Establish governance frameworks for data quality, model reliability, and compliance, ensuring that analytical outputs are auditable and trustworthy.
• Recruit, develop, and retain top data science talent, creating an environment that enables rigorous experimentation and production excellence.
• Partner with commercial and finance teams to design analytics that clarify value, track performance, and support strategic planning.
• Lead the evaluation and adoption of new data science tools and methodologies, balancing innovation with practical deployment constraints in healthcare.

Requirements

• Hold an MS or PhD in a quantitative field such as statistics, mathematics, computer science, or physics.
• Bring 12 or more years of experience in data science or analytics, with a proven track record in product-facing roles.
• Demonstrate depth of experience using Python, R, and SQL for large-scale analytics and complex data workflows.
• Show comfort building data capabilities from early exploration through rapid growth, including close collaboration with data engineering and machine learning teams.
• Have experience with data visualization tools, including both BI platforms like Tableau, Looker, or Sigma and code-based tools like Seaborn or ggplot2.
• Communicate quantitative findings clearly and compellingly to non-technical stakeholders, including executives and clinical partners.
• Exhibit strong leadership skills, with the ability to build high performing teams and drive execution in a fast-paced, growth oriented environment.
• Have a strong grasp of responsible AI practices, model evaluation, and performance monitoring in production settings.
• Understand the importance of structured, auditable insights that can be verified and trusted in clinical workflows.
• Be comfortable operating in an environment where ambiguity is high and decisions must be made with incomplete information while maintaining scientific rigor.

Nice to have

• Knowledge of healthcare settings, electronic health records (EHR), and healthcare billing.
• Experience shaping or recruiting for analytics engineering functions.

Practical notes

No additional hours, travel, visa, or deadline details are specified in the source.