Director, Development Analytics Strategy (Convergence Catalyst)
Posted on Apr 5, 2021 by Pharmacyclics
The Director will coordinate the execution of the (Clinical) Development Analytics strategy and vision by working with the functional area leadership and cross-functional stakeholders. Help define Convergence data integration strategies and advance Clinical Development priorities while maintaining alignment with the overarching R&D Convergence data integration framework and priorities.
- Act as a key member of the R&D Convergence (Data Integration) Core team responsible for defining and executing the R&D Convergence strategy, and represent Clinical Development in cross-functional R&D discussions and decision-making forums related to Convergence data integration.
- Collaborate with scientists, clinicians, and data scientists within Clinical Development and across R&D to identify and lead projects involving the aggregation and analysis of large data sets to identify key trends and insights that advance scientific understanding.
- Ensure that projects create a substantial return on investment and a meaningful impact on advancement of the AbbVie pipeline of new medicines and/key functional/therapeutic area or R&D priorities.
- Coordinate the execution of the (Clinical) Development Analytics strategy and vision by working with the functional area leadership and cross-functional stakeholders
- Drive the communication and change management across Development functions by highlighting the Development Analytics roadmap, progress, and the value generated.
- Influence applicable functional leadership, strategies, and investments in alignment with the R&D Convergence data integration vision.
- Translate functional/therapeutic area business challenges and scientific questions into hypotheses that can be tested using data analytics to drive new learning.
- Partner with R&D Computational Scientists, Data Scientists, and technology teams to advance complex and/or highly novel use-cases with cross-functional dependencies.
- Identify opportunities and solutions to unleash the full value of Convergence data integration within functional/therapeutic area at an accelerated pace.
- Lead Matrix teams including scientists, data scientists and MD's at all levels of the technical and medical career ladders.
- Lead Convergence data integration projects within functional/therapeutic area, allocate resources, manage the budget, and ensure appropriate area expertise.
- Act as a conduit between the R&D Convergence Core Team and subject matter experts and data scientists within Clinical Development to transfer innovation, knowledge, best practices, and lessons learned.
- An active member of the community of R&D Convergence Directors across other R&D functions and enable cross-pollination of ideas for broader R&D implementation.
- Act as a consultant within Clinical Development to help identify where Convergence data integrations and analytics approaches can help answer questions.
- Influence the talent strategy within Clinical Development to continually enhance the digital skills and acumen of scientists and leaders to align with the Convergence data integration strategy.
- Ph.D. with approximately 8-10 years of industry experience or MS with 12+ years of experience in a related field; at least 3 years of experience enabling data analytics and insights within Clinical Development
- Experience in multiple R&D functions and data domains (eg, preclinical, genomic, clinical, safety, regulatory, real world data, etc.). Considered to have in depth knowledge of drug discovery and development
- Demonstrated excellence and success in leading data integration and analytic programs to major milestones or insights
- Effective interpersonal and communication skills and proven ability to collaborate across multi-disciplinary teams and leading and navigating in a matrixed organization
- Robust understanding of the data analytics value chain from data to decision
- Proven experience with successfully enabling new digital or data science capabilities that are beyond incremental extensions of the current capabilities
- Advanced knowledge of machine learning techniques, predictive modeling, statistical methodologies, and visual analytics
- Experience with big data technologies such as Hadoop, Cloud Computing, and related technologies