Principal Research Scientist I

AbbVie•North Chicago, IL

16h

About The Position

AbbVie Analytical Research & Development (Analytical R&D) is a global analytical scientific organization responsible for CMC analytical activities for the development of AbbVie pharmaceutical products, spanning small-molecule medicines from Discovery candidate selection through Early-Stage Clinical Development. The Principal Research Scientist I will provide scientific and technical leadership for analytical data science, AI/ML, advanced analytics, and data automation within Analytical R&D. The role partners with analytical scientists to frame scientific questions, integrate complex data, and develop fit-for-purpose models, visualizations, applications, and digital solutions that support data-driven decisions across pharmaceutical development. It leads data-science and application-development work, mentors scientists, and provides leadership across projects and initiatives. The position translates between analytical scientists, statisticians, data engineers, software and IT partners, and converts concepts into sustainable, adopted solutions with measurable scientific and business impact. 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 partnerships with internal data-science, statistics, information technology, and R&D organizations. Solutions and activities must comply with applicable safety, quality, regulatory, data-integrity, privacy, security, and AbbVie requirements.

Requirements

Bachelor’s, Master’s, or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Chemometrics, Computational Science, Analytical Chemistry, Pharmaceutical Sciences, or a closely related discipline.
Typically, at least 14 years of relevant industry experience with a bachelor’s degree, 12 years with a master’s degree, or 6 years with a Ph.D.
Demonstrated experience applying data science, AI/ML, statistics, or advanced analytics to scientific, pharmaceutical, biotechnology, chemical, or other complex R&D problems.
Advanced proficiency in Python and/or R and strong working knowledge of relevant data-science libraries, statistical modeling, machine learning, data visualization, and reproducible analytical workflows.
Experience integrating, cleaning, transforming, and analyzing complex and heterogeneous data; working knowledge of SQL, relational data concepts, APIs, and modern data-management practices.
Demonstrated ability to build and deliver production-quality analytical applications, dashboards, automated workflows, or reusable data products.
Strong understanding of model development and evaluation, experimental design, uncertainty, validation, interpretability, and appropriate use and limitations of AI/ML methods.
Demonstrated leadership through mentoring, technical direction, project or portfolio leadership, cross-functional influence, and delivery of complex initiatives.
Ability to partner with domain scientists, learn scientific workflows, translate ambiguous needs into tractable analytical problems, and balance technical rigor with practical, fit-for-purpose delivery.
Experience with software-development practices such as Git/version control, modular coding, testing, documentation, code review, deployment, and lifecycle management.
Strong analytical problem-solving, project-management, written and oral communication, and stakeholder-management skills, including the ability to explain complex concepts to diverse audiences.
Self-motivated, collaborative, and adaptable, with scientific curiosity, creativity, sound judgment, and the ability to manage multiple priorities in a fast-paced R&D environment.

Nice To Haves

Experience with tools such as Streamlit, Shiny, cloud platforms, workflow automation, or equivalent technologies.
Prior direct people-management experience; beneficial but not required.
Knowledge of pharmaceutical development, CMC, analytical laboratory workflows, regulated environments, data integrity, or scientific data standards.
Experience with computational chemistry and molecular modeling to investigate molecular properties, structure–property relationships, or other pharmaceutical-development questions.

Responsibilities

Provide scientific and technical leadership for analytical data science, AI/ML, advanced analytics, visualization, and data automation supporting pharmaceutical development.
Consult with analytical scientists and project teams to define scientific or business questions, assess data readiness, choose analytical approaches, and translate results into recommendations and decisions.
Lead 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, ensuring they 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 applications, dashboards, reports, and automated workflows that reduce manual effort, improve consistency, and enable timely scientific insights.
Mentor, guide, and develop data scientists and scientific colleagues through technical coaching, peer review, hands-on support, and learning opportunities.
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 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, connecting teams with expertise and facilitating cross-functional execution.
Identify high-value opportunities, generate scientific proposals, evaluate emerging AI/ML and digital technologies, and champion responsible innovation.
Communicate complex methods and findings to technical and nontechnical audiences through presentations, reports, visualizations, and scientific publications where appropriate.
Ensure solutions and activities comply with applicable safety, quality, regulatory, data-integrity, privacy, security, and AbbVie requirements.