Manager - Pharma -Lifesciences -Health Research and Benchmarking- - TD

IT Project Manager – Pharma & Life Sciences (Health Research & Benchmarking)

Role Overview:

IT Project Manager

Pharma and Life Sciences domain

Health Research & Benchmarking

HTA, HEOR, RWE

comparative effectiveness research

Flatiron, IQVIA

Real-World Data (RWD)

Key Responsibilities:

• Lead end-to-end IT project management in health research and life sciences initiatives.
• Collaborate with cross-functional stakeholders including researchers, data scientists, compliance teams, and IT developers.
• Manage and deliver projects involving:
• Health Technology Assessment (HTA)
• Health Economics and Outcomes Research (HEOR)
• Real-World Evidence (RWE)
• Patient outcomes research
• Comparative effectiveness analysis
• Public health metrics
• Ensure regulatory compliance and data governance, particularly in handling real-world health data.
• Coordinate with external vendors and data providers such as IQVIA, Flatiron, and similar sources.
• Monitor project milestones, budgets, risks, and deliverables using industry-standard project management methodologies (Agile, Waterfall, etc.).
• Translate research needs into technical and functional project requirements.
• Facilitate stakeholder communication and produce clear project documentation.
  

Qualifications:

• Bachelor's or Master’s degree in Life Sciences, Computer Science, Public Health, or a related field.
• 10–15 years of experience in IT project management in the pharmaceutical or healthcare research sector.
• Proven experience managing projects involving HEOR, RWE, or HTA frameworks.
• Familiarity with real-world data platforms such as Flatiron Health, IQVIA, Optum, or similar.
• Strong understanding of clinical data standards, regulatory environments (e.g., HIPAA, GDPR), and health informatics.
• Project Management certification (e.g., PMP, PRINCE2) preferred.
• Excellent communication, stakeholder management, and analytical skills.
  

Preferred Skills:

• Experience working with epidemiologists, biostatisticians, or public health researchers.
• Knowledge of data integration or visualization platforms (e.g., Tableau, Power BI).
• Understanding of AI/ML applications in health research (optional but advantageous).
  

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