Senior Director, Data Development

Verana Health•San Francisco, CA

2d•$245,000 - $275,000•Hybrid

About The Position

Verana Health is a digital health company focused on delivering quality drug lifecycle and medical practice insights from a real-world data network. The company recently secured significant Series E funding. Verana Health is driven to create quality real-world data in ophthalmology, neurology, urology, and oncology to accelerate insights and advance patient care. The company culture is defined by DRIVE: Diversity, Responsibility, Integrity, Voice-of-Customer, and End-Results. The headquarters are in San Francisco, with additional offices in Knoxville, TN, and New York City. Employees can work remotely in various states, and permanent residency in one of these states is required. Candidates willing to relocate are encouraged to apply. Priority consideration will be given to candidates based in the Bay Area, New York City, and Knoxville, Tennessee, where the position will follow a hybrid work model (3 days in office, remaining days remote). Applicants must be authorized to work in the U.S. without sponsorship. This role involves leading the application of Machine Learning to transform clinical data into scalable healthcare data products. The Senior Director will report directly to the Chief Data & Science Officer and address complex real-world problems across therapeutic areas like urology, ophthalmology, and oncology. The team develops machine learning systems using structured and unstructured electronic health record (EHR) data, including clinical text and imaging data, to support Verana Qdata® products and real-world insight generation. Key activities include algorithm development, data pre- and post-processing, code implementation, modeling, inference, and interpretation. This role is crucial for ensuring data products are generated with fit-for-purpose approaches and meet quality requirements. The Senior Director will oversee a team working with heterogeneous clinical datasets from EHR systems and imaging workflows, emphasizing validation, explainability, and scientific quality. The Data & Science team aims to improve patient care by advancing the use of real-world clinical data, machine learning, and clinical expertise. This position is ideal for a leader who can translate machine learning advances into reliable, clinically meaningful systems for real-world healthcare environments.

Requirements

Master’s or doctorate in a quantitative discipline (e.g., data science, computer science, machine learning, biostatistics, biomedical informatics, etc.)
10+ years of experience applying machine learning to clinical or healthcare datasets, including recent experience deploying, fine-tuning, evaluating, or operationalizing foundation models and applied machine learning systems
5+ years managing technical teams
Strong programming experience using Python, SQL, PySpark, R, or similar tools
Experience working within modern cloud-based machine learning ecosystems and deployment environments (e.g., Databricks, SageMaker, MLflow, distributed compute platforms, or similar tooling)
Experience moving machine learning systems from experimentation into production environments, including model evaluation, monitoring, performance optimization, and cross-functional operationalization
Familiarity with machine learning approaches for clinical imaging data
Experience working with clinical datasets derived from EHR systems and related healthcare data sources
Clear communication skills and able to deliver internal/external presentations, particularly to executive teams
Ability to work effectively with cross-functional teams, including to inform data product roadmap
Ability to prioritize and manage multiple initiatives with high attention to detail

Responsibilities

Lead a team developing and operationalizing machine learning solutions for structured and unstructured healthcare data, including clinical text and imaging workflows
Guide the evaluation, fine-tuning, deployment, and monitoring of pretrained language models for healthcare applications
Help define technical strategy and development priorities for machine learning capabilities supporting Verana’s data products
Establish best practices for model lifecycle management, validation, reproducibility, explainability, and documentation
Partner cross-functionally with Product, Engineering, Medical, and Commercial teams to align technical solutions with scientific and business objectives
Drive high standards for data quality, model governance, and clinical relevance in production data products
Mentor and develop machine learning scientists and technical leaders across the organization