Leader of R&D Digitalization & Advanced Analytics
Listed on 2026-07-01
-
IT/Tech
Data Analyst, AI Engineer (Applied/Software), Data Science Manager, Data Scientist -
Research/Development
Data Scientist
Job Summary
The Head of R&D Digitalization & Advanced Analytics leads the end-to-end digital transformation of Global R&D, shaping the strategy and execution of digital, data, and automation initiatives across all R&D centers. This role is responsible for mapping scientific and operational processes, identifying opportunities to digitalize workflows, and enabling the effective use of data to accelerate development timelines, enhance decision-making, and increase productivity.
A key mandate of the position is to convert scientific and operational data into predictive insights that support advanced modeling, simulation, and virtual experimentation (such as virtual labs) to drive faster, more robust formulation and drug product development. The role serves as a strategic integrator between science, data, and technology, partnering closely with R&D functions, IT, external collaborators, and senior leadership to embed digital capabilities into everyday R&D execution.
Salary Range: $,000. Position is eligible to participate in an annual bonus plan with a target of 14% of the base salary. Final pay determinations will depend on various factors, including experience, education, knowledge, skills, and abilities.
Our benefits and programs are comprehensive and thoughtfully crafted to ensure our colleagues live healthy lives and have support when it matters most. Benefits include a 401(k) plan with company contributions, paid vacation, holiday and personal days, employee assistance program, and health coverage (medical, prescription drug, dental, and vision).
Responsibilities- R&D Digital Strategy & Roadmap
- Develop and own the global R&D digitalization strategy aligned with R&D and BU Pharma objectives.
- Map end-to-end R&D processes across all R&D centers (e.g., formulation, analytical, MS&T, feasibility, lifecycle) to identify automation and digitalization opportunities.
- Prioritize initiatives based on scientific impact, speed, scalability, and return on investment.
- Automation & Process Digitalization
- Identify repetitive, manual, or data-intensive R&D activities suitable for automation.
- Drive implementation of automation solutions (e.g., data capture, workflow automation, integration of lab systems).
- Establish global standards for digital tools and processes to ensure scalability and consistency across sites.
- Advanced Analytics, Modeling & Virtual Development
- Leverage R&D scientific data to build predictive models supporting formulation development, process optimization, and risk assessment.
- Enable simulation-based and data-driven development approaches, including virtual labs and in silico experimentation, to reduce experimental cycles and accelerate decision making.
- Partner with formulation, analytical, and MS&T experts to ensure models are scientifically sound and fit for purpose.
- Data & Digital Enablement
- Define R&D data architecture needs in collaboration with IT, ensuring accessibility, integrity, and regulatory compliance.
- Promote reuse of scientific data across projects and lifecycle stages.
- Embed digital tools into core R&D workflows rather than as standalone pilots.
- Cross Functional Leadership & Change Management
- Act as a change leader, driving adoption of digital tools and new ways of working across R&D sites.
- Build strong partnerships with R&D leaders, IT, Quality, Regulatory, and external technology partners.
- Develop digital and data capabilities within R&D through targeted upskilling and talent development.
- Governance & Value Tracking
- Establish clear governance for digital initiatives, including prioritization, milestones, and success metrics.
- Track and communicate impact (e.g., cycle time reduction, cost avoidance, quality improvement, scientific insight generation).
- Ensure digital investments deliver measurable business and scientific value.
- Key Interfaces
- Global R&D Leadership Team
- Heads of Formulation, Analytical, MS&T, Feasibility, and Lifecycle Management
- IT / Digital / Data Science teams
- Quality and Regulatory
- External technology and analytics partners
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