Principal Data Scientist, Data and AI Convergence

About the role

The Principal Data Scientist will serve as a strategic leader and technical expert driving enterprise-level transformation through the convergence of R&D data assets and the systematic integration of advanced AI/ML capabilities into organizational workflows.

Responsibilities

• Lead the identification and assessment of enterprise-level gaps in data convergence, analytical capabilities, and AI/ML integration across R&D workflows; design comprehensive strategies to address systemic challenges through scalable, integrated solutions
• Architect and champion collaborative cross-functional frameworks that enable the systematic integration of advanced AI/ML capabilities into existing R&D and clinical workflows, ensuring solutions are extensible, maintainable, and aligned with enterprise data strategies.
• Drive organizational transformation initiatives that fundamentally enhance how data and AI inform strategic decision-making across the R&D portfolio, therapeutic development, and clinical trial execution
• Establish and evangelize best practices, standards, and governance frameworks for enterprise-wide AI/ML workflow integration that ensure consistency, quality, and regulatory compliance
• Serve as the technical leader for high-impact, cross-functional initiatives requiring advanced data convergence and AI/ML integration across multiple therapeutic areas and R&D functions
• Partner with senior leadership across R&D, IT, Data Science, and business functions to align workflow transformation initiatives with strategic priorities and ensure executive-level buy-in
• Represent R&D in enterprise-wide forums and decision-making bodies related to data strategy, AI governance, and technology architecture, advocating for solutions that balance innovation with scalability and compliance
• Oversee development of reusable, modular AI/ML components and workflow templates that can be rapidly adapted across different therapeutic areas and functional domains
• Collaborate with Data Engineering, MLOps, and IT teams to establish robust infrastructure and platforms that support the scalable deployment and operation of integrated AI/ML workflows
• Establish metrics and monitoring frameworks to continuously assess the impact of AI/ML workflow integrations on R&D efficiency, decision quality, and strategic outcomes
• Maintain deep expertise in the latest advances in artificial intelligence, machine learning, and analytical methodologies; evaluate emerging technologies for their potential to drive enterprise-wide workflow transformation
• Ensure all AI/ML solutions adhere to regulatory requirements, data governance policies, ethical AI principles, and AbbVie quality standards
• Design solutions with security, privacy, auditability, and explainability considerations embedded from inception, particularly for regulated clinical and healthcare applications
• Translate complex technical architectures and AI/ML methodologies into clear strategic narratives for diverse stakeholders, from technical teams to executive leadership

Requirements

• PhD in Computer Science, Statistics, Bioinformatics, Computational Biology, Applied Mathematics, Data Science, or related quantitative field strongly preferred; Master's degree with exceptional demonstrated expertise and extensive experience considered
• 4-5+ years of progressive experience building, deploying, and scaling advanced AI/ML solutions in enterprise environments, with demonstrated leadership of large-scale, cross-functional data and analytics initiatives.
• Proven track record of leading enterprise-wide workflow projects that resulted in measurable organizational impact and sustainable capability development
• Minimum 5+ years of experience working in highly matrixed, complex organizational environments (Preferred experience in Consulting across pharmaceutical, biotech, healthcare, or similarly regulated industries strongly preferred)
• Expert-level proficiency in advanced machine learning and artificial intelligence, including deep learning, neural network architectures, ensemble methods, transfer learning, and generative AI technologies
• Demonstrated mastery of ML/AI frameworks and platforms (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face) and their application to complex, real-world problems
• Advanced programming capabilities in Python and R, with strong software engineering principles; experience with production code development, version control, CI/CD pipelines, and testing frameworks
• Deep understanding of MLOps principles, model lifecycle management, workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow), and enterprise deployment architectures
• Experience with cloud computing platforms (AWS, Azure, others) and distributed computing frameworks for large-scale data processing and model training
• Strong expertise in data architecture, data integration patterns, and modern data platforms supporting enterprise analytics
• Demonstrated success leading enterprise-scale initiatives that transform organizational workflows and decision-making processes through data and AI integration
• Proven ability to influence senior leadership, build cross-functional coalitions, and drive adoption of complex technical solutions across large, matrixed organizations
• Strong change management acumen and experience driving organizational transformation in regulated environments
• Track record of successful collaboration with multidisciplinary teams including data scientists, software engineers, clinicians, scientists, and business stakeholders
• Experience in pharmaceutical R&D, clinical development, or healthcare analytics strongly preferred
• Understanding of regulatory requirements, clinical trial design, drug development lifecycle, and healthcare data governance preferred
• Working knowledge of healthcare data standards (e.g., CDISC, OMOP) and FAIR data frameworks preferred

Qualifications

• PhD in Computer Science, Statistics, Bioinformatics, Computational Biology, Applied Mathematics, Data Science, or related quantitative field strongly preferred; Master's degree with exceptional demonstrated expertise and extensive experience considered
• 4-5+ years of progressive experience building, deploying, and scaling advanced AI/ML solutions in enterprise environments, with demonstrated leadership of large-scale, cross-functional data and analytics initiatives.
• Proven track record of leading enterprise-wide workflow projects that resulted in measurable organizational impact and sustainable capability development
• Minimum 5+ years of experience working in highly matrixed, complex organizational environments (Preferred experience in Consulting across pharmaceutical, biotech, healthcare, or similarly regulated industries strongly preferred)
• Expert-level proficiency in advanced machine learning and artificial intelligence, including deep learning, neural network architectures, ensemble methods, transfer learning, and generative AI technologies
• Demonstrated mastery of ML/AI frameworks and platforms (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face) and their application to complex, real-world problems
• Advanced programming capabilities in Python and R, with strong software engineering principles; experience with production code development, version control, CI/CD pipelines, and testing frameworks
• Deep understanding of MLOps principles, model lifecycle management, workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow), and enterprise deployment architectures
• Experience with cloud computing platforms (AWS, Azure, others) and distributed computing frameworks for large-scale data processing and model training
• Strong expertise in data architecture, data integration patterns, and modern data platforms supporting enterprise analytics
• Demonstrated success leading enterprise-scale initiatives that transform organizational workflows and decision-making processes through data and AI integration
• Proven ability to influence senior leadership, build cross-functional coalitions, and drive adoption of complex technical solutions across large, matrixed organizations
• Strong change management acumen and experience driving organizational transformation in regulated environments
• Track record of successful collaboration with multidisciplinary teams including data scientists, software engineers, clinicians, scientists, and business stakeholders
• Experience in pharmaceutical R&D, clinical development, or healthcare analytics strongly preferred
• Understanding of regulatory requirements, clinical trial design, drug development lifecycle, and healthcare data governance preferred
• Working knowledge of healthcare data standards (e.g., CDISC, OMOP) and FAIR data frameworks preferred

Skills

• Expert-level proficiency in advanced machine learning and artificial intelligence, including deep learning, neural network architectures, ensemble methods, transfer learning, and generative AI technologies
• Demonstrated mastery of ML/AI frameworks and platforms (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face) and their application to complex, real-world problems
• Advanced programming capabilities in Python and R, with strong software engineering principles; experience with production code development, version control, CI/CD pipelines, and testing frameworks
• Deep understanding of MLOps principles, model lifecycle management, workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow), and enterprise deployment architectures
• Experience with cloud computing platforms (AWS, Azure, others) and distributed computing frameworks for large-scale data processing and model training
• Strong expertise in data architecture, data integration patterns, and modern data platforms supporting enterprise analytics
• Demonstrated success leading enterprise-scale initiatives that transform organizational workflows and decision-making processes through data and AI integration
• Proven ability to influence senior leadership, build cross-functional coalitions, and drive adoption of complex technical solutions across large, matrixed organizations
• Strong change management acumen and experience driving organizational transformation in regulated environments
• Track record of successful collaboration with multidisciplinary teams including data scientists, software engineers, clinicians, scientists, and business stakeholders
• Experience in pharmaceutical R&D, clinical development, or healthcare analytics strongly preferred
• Understanding of regulatory requirements, clinical trial design, drug development lifecycle, and healthcare data governance preferred
• Working knowledge of healthcare data standards (e.g., CDISC, OMOP) and FAIR data frameworks preferred

Benefits

AbbVie offers a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.

Pay

$141,500 - $268,500 USD

Schedule

Hybrid