Principal Data Scientist, Data and AI Convergence

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
• Oversee the development of sophisticated, production-grade AI/ML workflow orchestrations that integrate multiple data sources, analytical techniques, and decision support capabilities into cohesive enterprise solutions
• Lead the collaborative application of state-of-the-art AI technologies including advanced machine learning, deep learning architectures, natural language processing, and generative AI to transform complex R&D and clinical development processes
• Architect end-to-end analytical pipelines that seamlessly connect data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement within enterprise platforms.
• Drive innovation in workflow automation and intelligent process optimization, leveraging AI/ML to reduce cycle times, enhance quality, and improve decision accuracy across the R&D continuum
• 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

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