Principal Research Scientist I

Job Description

AbbVie Analytical Research & Development (Analytical R&D) is a global analytical scientific organization responsible for all CMC analytical activities related to the development of AbbVie's pharmaceutical products. Our scope includes AbbVie's diverse pipeline of innovative small molecule medicines and spans from the selection of Discovery candidate molecules through Early-Stage Clinical Development. We are building a world-class team of scientists and leaders who will drive the advancement of industry-leading analytical capabilities and make meaningful contributions to advance AbbVie's pipeline of innovative medicines. Careers in AbbVie Analytical R&D provide unique opportunities to work with global and cross-functional teams to drive exceptional science and innovative product development strategies. We are looking for diverse candidates with strong scientific and leadership experience to join the AbbVie Analytical R&D team and can offer exciting and rewarding career opportunities for these candidates.

Position Description:

For the Principal Research Scientist I position, we are seeking a highly motivated and experienced data scientist to provide scientific and technical leadership for analytical data science, artificial intelligence/machine learning (AI/ML), advanced analytics, and data automation within Analytical R&D. The successful candidate will partner closely with analytical scientists to frame high-value scientific questions, engineer and integrate complex analytical data, and develop fit-for-purpose models, visualizations, and digital solutions that accelerate data-driven decisions across pharmaceutical development.

The candidate will perform and direct data science and application-development activities, mentor and develop scientists, and provide leadership across multiple projects and initiatives. They will conceive, execute, and communicate multidisciplinary strategies; translate between analytical scientists, statisticians, data engineers, software and IT partners; and convert innovative concepts into sustainable, adopted solutions with measurable scientific and business impact.

This role requires a strong combination of data science depth, pharmaceutical or scientific domain awareness, product-oriented solution development, and collaborative leadership. The scientist will help shape the Analytical R&D data science strategy, establish reusable standards and best practices, coordinate initiatives across a global network, and foster productive partnerships with internal data science, statistics, information technology, and R&D organizations.

Responsibilities:

• Provide scientific and technical leadership for analytical data science, AI/ML, advanced analytics, visualization, and data automation initiatives supporting pharmaceutical development.
• Consult with analytical scientists and project teams to define the scientific or business question, assess data readiness, select appropriate analytical approaches, and translate results into clear recommendations and decisions.
• Lead the end-to-end delivery of data products and analytical solutions, including problem framing, data acquisition and integration, exploratory analysis, feature engineering, model development and validation, visualization, deployment, documentation, user adoption, and lifecycle support.
• Develop and apply statistical, machine-learning, chemometric, and/or generative-AI methods to complex analytical data while ensuring approaches are scientifically sound, interpretable, reproducible, and fit for intended use.
• Engineer, curate, and analyze structured and unstructured data from laboratory instruments, scientific systems, documents, and development workflows; improve data quality, traceability, accessibility, and reuse.
• Design and deliver intuitive applications, dashboards, reports, and automated workflows that reduce manual effort, improve consistency, and enable scientists to obtain timely insights from their data.
• Mentor, guide, and develop data scientists and scientific colleagues; provide technical coaching, peer review, and hands-on support, and create learning opportunities that strengthen data literacy and analytical capability across the organization.
• Lead or coordinate a portfolio of data science initiatives, establish priorities and delivery plans, manage risks and dependencies, and communicate progress, value, and recommendations to project teams and senior stakeholders.
• Establish and promote standards for analytical data assessment, model validation, software development, documentation, version control, testing, data governance, and sustainable solution support.
• Build and foster a global network with Analytical R&D, Development Sciences, statistics, enterprise data science, IT, and external partners; connect teams to the appropriate expertise and facilitate effective cross-functional execution.

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