Medical Analytics Consultant – Evidence Generation

Title:

Medical Analytics, Evidence Generation (AD)

Duration:
12 Months Contract

Location:

Basking Ridge, NJ

Shift:
Remote - Ability to travel up to 10%

Job Summary

The Contractor, Medical Analytics, Evidence Generation will partner with Global and regional Medical Affairs, R&D, Technology Operations, IT, Change Enablement, and Evidence Planning Subject Matter Experts to design, implement, and advance end-to-end analytical solutions that support evidence planning.

This role owns the analytical framing, methods, metrics, interpretation, validation, and decision support for Evidence Generation digital capabilities. The role will help connect strategic imperatives to evidence gaps, study concepts, studies, and study status, and use these insights to support decision-making and assess the impact of evidence generation activities.

Work Location

• Candidate can work 100% remotely for the duration of the assignment.
• Ability to travel up to 10% (mostly to Basking Ridge, NJ).

Responsibilities
1. Analytical Discovery and Requirements Definition

• Translate evidence planning needs into analytical solutions by working with GOMA, regional Medical Affairs, R&D, and Evidence Planning SMEs to define business questions, workflows, data requirements, and what good looks like for evidence planning decision support.
• Lead analytical working sessions with Evidence Planning SMEs and cross-functional partners to define analytical questions, data needs, metric logic, decision criteria, and data-quality requirements for Evidence Generation dashboards and data products.
• Lead the analytical assessment of the current IEP dashboard and define the analytical framework and requirements for enhanced evidence planning capability that maps strategic imperatives to evidence gaps, study concepts, studies, and study status, including planned, ongoing, completed, and published studies. Assess and recommend potential AI/advanced analytics use cases based on feasibility, data availability, expected value, and limitations, in partnership with the Technology Implementation and IT teams.

2.

Analytical Design and Data Foundation

• Develop analytical specifications and acceptance criteria for Evidence Generation dashboards and data products, including the analytical data model, required data elements, metric calculations, analytical business rules, data-quality expectations, analytical views, and documented assumptions. Partner with business leads and the Technology Implementation team to establish common definitions, taxonomies, metadata, status conventions, data ownership, refresh requirements, and data-quality expectations needed to implement the analytical specifications and support scalable evidence planning.
• Define analytical requirements for integrating Evidence Generation data into the Medical Data Foundation, including data source needs, metric continuity, and analytical data quality, enabling Technology Implementation and IT teams to create the core and analytical data pipeline needed for the final visualization.

3. Insights and Evidence Activity Impact Measurement

• Generate actionable insights from dashboard outputs to help stakeholders identify evidence gaps, overlaps, dependencies, timing considerations, and opportunities to support evidence planning, prioritization, and portfolio oversight.
• Leverage evidence planning dashboard outputs to support Medical Affairs impact measurement by linking evidence generation activities to prioritized medical strategies, leading indicators on HCP knowledge/behavior gaps, lagging indicators on patient care gaps, and other agreed measures of impact.
• Define analytical measures and reporting requirements that support adoption and value realization, including data completeness, analytical output quality, decision use, and other agreed indicators. Partner with Change Enablement to interpret adoption and engagement data and inform improvement activities.

4. Analytical Validation and Product Improvement

• Lead analytical validation of prototypes and outputs, testing calculation, data behavior, visualizations and interpretation to confirm proposed outputs answer the intended decision questions, are accurate and interpretable, and support Evidence Generation decisions.
• Document analytical findings and recommendations for project teams to incorporate product improvements.

5. Delivery, Governance and Responsible Data Use

• Provide timely analytical inputs to the integrated project plan, including milestone estimates, analytical risks, and dependencies to the…