Live opening · Posted 7 hours ago

Associate Director, Data and Digital Strategy

AbbVie · Worcester, MA, United States
Smartrecruiters Hybrid Full-time
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At a glance

The key details from the original listing.

Posted 7 hours ago
CompanyAbbVie
LocationWorcester, MA, United States
Job typeFull-time
Work modeHybrid
SkillsPython, AWS, GCP, TensorFlow, PyTorch
SourceSmartrecruiters
ListedPosted 7 hours ago

About the role

Description supplied by the original job listing.

This role leads digital transformation for PDS&T Biologics laboratory operations. The successful candidate will design, develop, and execute a comprehensive data and digital strategy that delivers measurable value to scientists and accelerates biologics development.
As a member of the Center of Excellence (COE) leadership team for digital transformation and ML/AI within PDS&T Biologics, this leader will shape the AI and digital strategy and drive end-to-end digital integration across Biologics CMC. The COE includes representatives from each functional group. It serves scientists' needs and defines the data systems architecture that underpins knowledge management, advanced analytics, and predictive modeling.
Working closely with IT partners (IR and BTS), this leader will evaluate and optimize data systems, including ELN, sample management, laboratory automation, and scientific data platforms. The aim is seamless, automated capture and connectivity of biologics process and analytical data in line with FAIR data principles. The leader will also establish business and data governance with other PDS&T divisions, Development Sciences, Manufacturing, and Quality. They will prioritize use cases and workflow applications that advance digital maturity and measure impact through clear KPIs.
Responsibilities
Provide leadership and subject-matter expertise for laboratory digital transformation, defining future-state capabilities and scalable digital solutions across PDS&T Biologics Development.
Drive integration of laboratory instruments and systems to automate data capture and reduce manual data entry across scientific workflows.
Serve as the digital product leader for ELN, sample management, laboratory automation, and scientific data platforms, translating scientific and business needs into scalable solutions.
Advance FAIR data principles and data standardization to improve the findability, accessibility, interoperability, and reuse of scientific data for analytics, modeling, and digital applications.
Own the continuous evolution of laboratory digital products, including system configuration, user experience, platform scalability, performance, and adoption.
Partner with scientists, laboratory operations, Quality, Technical Operations, Data Science, IR, and BTS to streamline workflows, improve data quality, and accelerate R&D outcomes.
Define KPIs and dashboards and communicate progress on initiatives to stakeholders.
Influence leadership and cross-functional stakeholders to secure investments that align digital transformation with PDS&T Biologics business priorities.
Represent scientists' needs and champion the work processes and culture that digital transformation requires.
Support the COE book of work for digital transformation within PDS&T Biologics.
Benchmark internally and externally to keep the data and digital strategy for biologics process and analytical development industry leading.
M.S. or Ph.D. in life sciences or a science-based informatics discipline, with 10+ years of pharmaceutical/biotechnology industry experience, including at least 8 years in a relevant R&D or Operations function.
Proven track record of enabling new digital or data management capabilities, beyond incremental improvements or routine system implementations.
Technical proficiency in programming languages (e.g., Python, R) and cloud platforms (e.g., AWS, GCP).
Familiarity with AI/ML platforms and tools (e.g., TensorFlow, PyTorch) relevant to biologics CMC workflows.
Demonstrated ability to apply AI/ML techniques in biologics CMC development to optimize processes, strengthen data analysis, and enhance predictive modeling.

Employment type
Full-time

Work arrangement
Hybrid

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