Job Description:

The Director, Digital Medicine & Translational Imaging Statistics is responsible for ensuring sound statistical thinking and methods are utilized in the discovery and development of novel digital and imaging endpoints and for bringing the principles of objective decision-making into the development, validation, and implementation of industry-leading digital health technology and imaging programs for incorporation into clinical trials across Pfizer’s portfolio. The Director is an integral member of a matrixed team, which develops and employs computational and statistical approaches to discover and validate digital and imaging endpoints and biomarkers. The Director must possess the ability to plan, direct and coordinate a variety of specialized and complex projects, must have knowledge of study design of experiments, data management and programming tools, and ability to interpret results from clinical studies. The Director should have ability to roll out new initiatives and assists in strategic planning. The Director should be a person of vision, should stay current on new developments and technological advancement in statistics. This person should be highly motivated and should possess excellent written and verbal communication skills.

Job Responsibilities:

Collaborate with interdisciplinary teams including clinicians, data scientists and data managers to develop, validate and deploy algorithms and analysis pipelines to derive digital endpoints for use in clinical trials.
Bring innovative statistical thinking and methods to help drive data-driven drug discovery and development employing modern methods such as machine learning/AI, longitudinal and time series methods, multivariate and functional data analysis approaches, and Bayesian methodologies.
Ensure rigorous approaches are taken and good scientific practices are followed: excellent statistical methods utilized and documented in protocols, analysis plans and manuscripts, and assay methods are appropriately blinded, randomized and designed to meet clear study objectives.
Lead novel statistical methodology projects with internal and external partners to advance the development of digital and imaging endpoints for use in clinical trials.
Provide statistical input and leadership to determine strategy and deliver results in a timely and high-quality manner. Ensure that all statistics activities are conducted in compliance with relevant regulatory requirements and Pfizer standards.
Provide planning and communication of statistical analyses, data presentations and scientific reports, including clinical trial results, exploratory analyses and analyses that synthesize results across studies, support for publication activities, scientific presentations, and regulatory interactions.
Interact with internal and external experts to assure sound quantitative approaches are applied to the collection and analysis of a wide variety of data types, including digital health technology data, imaging, and blood-based biomarkers.
Serve as the lead statistician on clinical trials incorporating novel AI/ML-based technologies and novel study designs such as decentralized clinical trials.
Demonstrate leadership experience and ability to lead teams and mentor colleagues.

Job Requirements/Qualifications:

Experience in clinical statistics supporting early / late-stage clinical development and/or real-world evidence studies.
Programming in Python
PhD in Statistics, Biostatistics or in quantitative discipline such as Physics, Applied Mathematics, Bioengineering, Electrical Engineering coupled with high level of statistical expertise and 7 years or more of industrial or similar experience, or Masters in the same fields and 9 years or more of industrial or similar experience.
Experience analyzing digital health technologies data, such as accelerometry, wearable devices and mobile app data.
Research experience with mathematical/statistical modeling using complex data.
Strong background in experimental design and statistical analysis including good understanding of inference and probability, competence in contemporary linear and predictive modeling including (longitudinal) mixed models, nonlinear regression, and predictive modeling.
Technical Skills:
Expert level knowledge of machine learning algorithms, feature selection and optimization.
Genuine interest in biology and pharmaceutical development, with the attitude of self-directed scientist; demonstrated ability to multitask.
Outstanding communication skills; ability to explain statistical and modeling concepts to non-experts.
Fluency in R and SAS programming
Demonstrated ability to work effectively independently and as a part of a team.