Senior Advanced Analytics & Insights Manager New Framingham, MA
Listed on 2026-08-04
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IT/Tech
Data Analyst, Data Science Manager, Data Scientist, Business Systems & Technology Analysis
Senior Advanced Analytics & Insights Manager
Framingham, MA
About Definitive Healthcare :
At Definitive Healthcare (NASDAQ: DH), we're passionate about turning data, analytics, and expertise into meaningful intelligence that helps our customers achieve success and shape the future of healthcare. We empower them to uncover the right markets, opportunities, and people- paving the way for smarter decisions and greater impact.
Headquartered just outside of Boston, Massachusetts, Definitive Healthcare operates across North America, Europe, and India, supporting a growing global client base of more than 2,400 customers since our founding in 2011.
We're also a great place to work. In 2024 and 2025, we earned multiple workplace honors, including Built In's 100 Best Places to Work in Boston (both years), a Stevie Bronze Award for Great Employers, and recognition as a Great Place to Work in India.
We foster a collaborative, inclusive culture where diverse perspectives drive innovation. Through programs like Definitive Cares and our employee-led affinity groups we strive to promote connection, education, and inclusion.
About the Role
We are looking for a Senior Advanced Analytics & Insights Manager with 5+ years of experience to support high-impact analytics work across healthcare and life sciences. This is a hands-on role for someone who can work directly with complex healthcare datasets, apply data science methods, write strong SQL and Python, and translate analytical outputs into clear business recommendations. The ideal candidate will combine technical execution with consulting-style problem solving.
They should be comfortable owning analytical work streams, guiding junior team members, partnering with business stakeholders, and delivering client-ready insights across use cases such as patient journeys, market assessment, targeting, segmentation, forecasting, real-world data analysis, and commercial strategy.
What You’ll Do
- Lead analytics work streams from business question definition through data analysis, modeling, interpretation, and final delivery.
- Work hands-on with large healthcare and life sciences datasets, including claims, provider, patient, market, commercial, or other real-world data.
- Write efficient SQL to extract, join, transform, validate, and analyze large datasets.
- Use Python for data wrangling, automation, statistical analysis, modeling, and repeatable analytical workflows.
- Apply appropriate data science techniques such as segmentation, forecasting, classification, regression, clustering, propensity modeling, or other statistical methods.
- Translate ambiguous client or business asks into structured analytical approaches, assumptions, outputs, and timelines.
- Build clear, client-ready deliverables in PowerPoint, Excel, dashboards, or other reporting formats.
- Perform QA on data, code, methodology, and outputs to ensure accuracy and defensibility.
- Support basic data engineering tasks such as dataset creation, data cleaning, feature generation, pipeline logic, and workflow automation.
- Work with cloud-based data platforms, notebooks, compute servers, and shared analytics environments.
- Mentor analysts and junior data scientists on SQL, Python, analytical methods, documentation, and quality control.
- Partner with cross-functional teams across analytics, product, engineering, consulting, and client-facing functions.
What You'll Bring
- 5+ years of experience in data science, advanced analytics, healthcare analytics, life sciences analytics, analytics consulting, or a related quantitative role.
- Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, Economics, Public Health, or a related quantitative field.
- Strong hands-on SQL experience, including complex joins, CTEs, aggregations, window functions, data validation, and performance-aware querying.
- Strong hands-on Python experience using libraries such as pandas, Num Py, scikit-learn, stats models, or similar tools.
- Experience working with large, complex datasets and turning them into actionable insights.
- Practical understanding of data science methods and when to apply them to business problems.
- Basic understanding of data engineering…
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