Job Description

Key Responsibilities:

• Provide coordination support to the Project Management and Clinical functional teams as assigned to ensure completion of deliverables that are balanced for quality, timeliness and resources.
• Assist Program Manager in providing planning and resource management support to the Compound Development Teams (CDTs).
• Facilitate team communications by maintaining and tracking cross-functional timelines, deliverables, and milestones including cross-project dependencies.
• Facilitate critical path analysis and optimization planning. Assist team in determining schedule and resource requirements.
• Maintain cross-functional project plans, team reports, and resource management support to ensure the completion of deliverables balanced for quality and timeliness.
• Generate program reports and communications to ensure team and program alignment of deliverable expectations.
• Assist functions in determining schedule and resource requirements.
• Conduct contingency planning/scenario analyses and propose strategies and solutions to modify the schedule.
• Conduct scope control analyses from a planning and forecasting perspective and translate the data to insights.
• Assist R&D leadership team through participation in initiatives that improve the R&D Project Planning footprint in the data sciences arena.

Qualifications

Education:

Minimum of a Bachelor’s degree; a postgraduate degree is preferred. Professional project management certification is preferred.

Experience and Skills:

Required:

• A minimum of 4 years of industry experience is required
• A minimum of 3 years of Drug Development experience and a detailed knowledge of the Drug Development process is required
• Detailed knowledge of project planning, tracking, scheduling tools, and cross-project analyses is required
• Understanding of clinical development is required
• Experience in MS Project, Planisware, or similar scheduling tool is required

Preferred:

• Detailed knowledge of or experience within the data sciences disciplines, extracting knowledge and insights from structured and unstructured data is preferred. This may include design thinking, robotic process automation, Artificial Intelligence, data mining, predictive analytics, and/or big data analytics.