The Associate Director, Digital Medicine Statistics, Early Clinical Development is responsible for ensuring sound statistical thinking and methods are utilized in the discovery and development of novel digital endpoints; bringing the principles of objective decision-making into the development, validation, and implementation of industry-leading digital health technology programs for incorporation into clinical trials across Pfizer's portfolio; and is an integral member of a matrixed team, which develops and employs computational and statistical approaches to discover and validate digital endpoints and biomarkers.ROLE RESPONSIBILITIES
- Demonstrate leadership experience and ability to influence and collaborate with peers and mentor others, and to oversee the work of colleagues to create business impact.
- Lead novel statistical methodology projects with internal and external partners to advance the development of digital endpoints for use in clinical trials.
- Collaborate with clinical teams and scientists in the design, analysis and reporting of clinical studies incorporating digital health technologies.
- Work with scientists to understand the biology and improve existing or derive new digital endpoints and develop 'fit-for-purpose' statistical models.
- Collaborate with interdisciplinary teams including clinicians, data scientists and data managers to develop, validate and deploy algorithms and analysis pipelines.
- 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.
- Core member of scientific teams responsible for determining strategy and delivering results in a timely and high-quality manner.
- 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.
- 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.
- Use statistical expertise to prepare both internal and external reports, presentations, manuscripts, and documents for regulatory interactions.
Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact. Basic Qualifications
- MSc in Statistics, Biostatistics or in quantitative discipline such as Physics, Applied Mathematics, Bioengineering, Electrical Engineering coupled with high level of statistical expertise. Research experience with mathematical/statistical modeling using complex data and six years or more of industrial or similar experience.
- Technical Skills: Fluency in R programming
- 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.
- Pharmaceutical applications desired, with early clinical (phase I and phase II) and translational experience a definite advantage.
- 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.
- Demonstrated ability to work effectively independently and as a part of a team.
NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS
- PhD in Statistics, Biostatistics or in quantitative discipline such as Physics, Applied Mathematics, Bioengineering, Electrical Engineering coupled with high level of statistical expertise.
- Research experience with mathematical/statistical modeling using complex data and two or more years of postdoctoral, industrial, or similar experience.
- Technical Skills: Programming in Python; knowledge of SAS and MATLAB; and prior use of cloud computing tools, e.g., AWS
- Experience analyzing digital health technologies data, such as accelerometry, wearable devices and mobile app data.
- Knowledge of methods of signal processing including electrophysiological data analysis.
- Knowledge of functional data analysis approaches and Bayesian methods.
Occasional travel required Other Job Details
Eligible for Relocation Package
Eligible for Employee Referral BonusSunshine Act
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