You will lead and develop a team of data analysts responsible for creating, validating, and continuously refreshing healthcare market datasets. You'll mentor your analysts in data methodology, statistical reasoning, and reproducible analytics practices, while establishing coding standards and documentation expectations so every output is scalable and reliable. You'll set up governance processes covering versioning, auditability, and traceability of data sources, and oversee change-detection logic so shifts in the market dataset are systematically captured. You'll define data quality standards across ingestion, transformation, and analysis workflows, and partner closely with Data Engineering and Platform teams to ensure the underlying infrastructure supports your team's work. You'll translate analytic outputs into business insights, benchmarks, and structured datasets that support product strategy, commercial enablement, and thought leadership, while driving project scoping, effort estimation, and planning with cross-functional partners. You'll also conduct code reviews and methodology reviews to keep the team's data workflows maintainable and high quality.
Responsibilities
- Lead and develop a team of data analysts responsible for the creation, validation, and ongoing refresh of healthcare market datasets
- Mentor analysts in data methodology, statistical reasoning, and reproducible analytics practices
- Establish analytic standards, coding practices, and documentation expectations for reproducible and scalable datasets
- Establish and maintain governance processes for market data including versioning, documentation, auditability, and traceability
- Oversee change-detection logic to identify and document additions, removals, and shifts in market datasets
- Define and enforce data quality standards across ingestion, transformation, and analysis workflows
- Collaborate with Data Engineering and Platform teams to ensure scalable data infrastructure
- Translate analytic outputs into business insights, benchmarks, and structured datasets
- Drive project scope definition, effort estimation, and planning with cross-functional teams
- Conduct code reviews and analytics methodology reviews
Requirements
- 7+ years of professional experience in data analytics, data science, or quantitative analysis in a production environment
- Strong proficiency in Python and data analysis libraries (Pandas, NumPy, SciPy, Matplotlib)
- Advanced SQL querying and ability to work with large datasets
- Strong understanding of statistics, data analysis methods, and pattern detection in complex datasets
- Experience managing multiple concurrent analytics projects and prioritizing work across a team of analysts
- Experience designing analytic frameworks or benchmark datasets used by business teams or external stakeholders
- Experience managing data quality, data governance, and dataset lifecycle management
- Experience evaluating and applying modern analytics or AI techniques (including LLM-based tools) to enrich datasets or accelerate analytic workflows
- Ability to experiment and rapidly prototype design frameworks and validate hypotheses efficiently
- Strong software engineering fundamentals including modular Python code, reusable analytic functions, and maintainable data pipelines
- Familiarity with cloud data platforms (e.g., AWS, GCP, or Azure) and common data services
- Ability to work seamlessly across Data Science, Engineering, Product, DevOps, and Data Engineering teams
- Excellent communication and storytelling skills to translate complex AI/ML concepts for stakeholders
- Ownership mindset with end-to-end accountability from concept to deployment to monitoring