Associate Director, Digital Quantitative Sciences

ROLE SUMMARY

The Associate Director, Digital Quantitative Sciences is responsible for ensuring sound statistical thinking and methods are utilized in the discovery and development of novel digital and imaging 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 and imaging endpoints and biomarkers.

This role sits in Pfizer’s AI/ML, Quantitative & Digital Sciences (AQDS) team within Global Biometrics and Data Management, Pfizer Research & Development, leveraging multi-modal data, digital measures, and the power of quantitative science to inform trial designs, support clinical and regulatory strategy and build patient-centric health solutions. The successful candidate will partner closely with Translational Clinical Sciences, Biomeasures, Endpoints & Study Technology team to develop novel measures that advance drug development.

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 and imaging 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 to derive digital endpoints for use in clinical trials.

• 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 interaction.

QUALIFICATIONS

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.

Preferred Qualifications

• PhD in Statistics, Biostatistics or in quantitative discipline such as Physics, Applied Mathematics, Bioengineering, Ele