MMIT is seeking experienced Data Scientists to meaningful contributes to the growth and development of the MMIT product portfolio. Ideal candidates are comfortable working with large, complex data sets to help inform, simplify and actualize key product capabilities. They will have comfort collaborating with various stakeholders and navigating ambiguity with limited direction. They will dedicate themselves to understanding the MMIT product portfolio (both current capabilities and future product roadmaps) and key data dependencies to inform data models, architecture, and new product development.
To succeed in this role, you will apply your skillset to
- Inform, create and/or maintain master data hierarchy, attributes and structure.
- Bridge claims data and coverage data – Refine existing rules and create and continually improve new methods for accurately assigning pharmacy and medical claims volumes to MMIT controller entities.
- Apply data science to the creation of short and long form geographic market and controller entity descriptive narratives (NLP / NLG)
- Translate product management strategy into actionable data science deliverables, guiding development of data models, business rules and other advanced analytical capabilities.
- Excel at communicating across a broad set of cross-functional stakeholders.
- Work collaboratively with MMIT product and data teams to develop and/or commercialize analytics use cases that may include market access, coverage / lives, and RWE data.
- Effectively partner with external organizations including clients and vendors to develop meaning and projectable analytics about trends in market access dynamics across payers, geographies, indications, drugs, etc.
- Identify and evaluate additional data sources that could be integrated in value enhancing ways.
- Build reports, dashboards and/or other analyses as needed and/or as specific initiatives arise.
- Support various commercial or commercialization efforts as needed.
- Take a leading role in developing data science as a discipline across product management, development and data operations.