Lead Data Scientist

Lead Data Scientist

In this role, you will leverage advanced statistical techniques, predictive modeling, and practical machine learning to address complex product and business challenges. You will collaborate cross-functionally with Product, Engineering, and Clinical stakeholders to design analytical solutions, assess outcomes, and build scalable data-driven systems that shape both platform capabilities and strategic direction. While the focus is on applied analytics and statistical modeling, exposure to generative AI and large language models is considered a valuable addition.

This position is well-suited for a highly analytical professional who enjoys solving meaningful, real-world problems and translating data into actionable outcomes within a healthcare-focused environment.

Key Responsibilities

• Lead end-to-end analytics initiatives that inform product development, organizational strategy, and clinical use cases
• Build and refine predictive models to better understand clinician behavior, engagement trends, and how usage varies across specialties and care environments
• Explore and analyze large-scale datasets, including user activity and healthcare-related data, to identify trends, insights, and improvement opportunities
• Develop scalable data infrastructure, including pipelines, workflows, and reporting tools, to support ongoing growth and decision-making
• Collaborate closely with cross-functional teams to transform analytical insights into product features and measurable business outcomes
• Design and manage experimentation frameworks, including A/B testing, to evaluate and improve product performance
• Establish and maintain best practices for data quality, reliability, and reproducibility across analytical work
• Present findings and recommendations to senior stakeholders, helping guide strategy and decision-making

Qualifications & Experience

• Degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related quantitative field (Bachelor’s required; advanced degree preferred)
• Extensive experience (typically 10+ years) in applied data science, predictive analytics, or statistical modeling, ideally within healthcare or similar data-rich domains
• Strong expertise in statistical analysis, machine learning methodologies, and predictive modeling techniques
• Proficiency in Python or R and commonly used data science libraries (e.g., pandas, NumPy, scikit-learn, statsmodels)
• Advanced SQL skills with experience working on large, complex datasets
• Demonstrated experience building, validating, and deploying machine learning models in production environments
• Proven ability to design experiments, conduct A/B testing, and interpret results with statistical rigor
• Excellent communication skills with the ability to clearly explain technical findings to both technical and non-technical audiences
• Experience with BI and data visualization tools (such as Tableau or Looker) to effectively communicate insights
• Deep understanding of machine learning approaches including classification, regression, clustering, recommendation systems, and causal inference
• Track record of driving data initiatives from concept through execution and influencing product direction
• Familiarity with healthcare or life sciences data (e.g., provider behavior, clinical usage data, claims, or decision support systems) is strongly preferred