Duties: Provide expert statistical support to EvGen leads and scientists in the design, analysis, reporting, and interpretation of RWD and in HEOR projects involving observational studies, including those in development supporting drug approval and reimbursement, HTA, as well as other post-approval activities, in order to inform decision-making and also to guide medical strategy and the scientific narrative through publications and external presentations; Partner with EvGen leads and scientists, other RWD statisticians, clinical statisticians, HEOR leads, clinicians, statistical programmers, and others to develop and implement an effective plan to leverage RWD
Requirements: Ph.D. in biostatistics or related discipline. A minimum of 3 years of experience with RWD and observational studies

Duties: Provide AI/ML and computational biology expertise to support Vaccine research & Development; Develop an integrated framework connecting multi-omics data analysis and ML algorithm to enhance vaccine target selection and optimization strategy; Work with platform leadership to shape and inform key RNA strategy and execution; Actively identify opportunities to leverage latest advances in AI/ML application; Communicate study findings efficiently and present data at various meetings as projects progress; Collaborate extensively with those from other scientific disciplines from within the group, from other subdivisions of Vaccines R&D, from other divisions of Pfizer, and potentially from external partners
Requirements: PhD in Systems Biology, Computer Science, Mathematics, or related quantitative field; 6+ years of experience developing and applying Machine Learning to address challenge biological problems; Foundational knowledge of ML methods and underlying mathematical concepts; Research experience related to next generation RNA therapeutics design and drug discovery; Proficiency with deep learning frameworks and data structures

Duties: Work effectively in cross-functional teams and provide computational biology expertise to advance drug discovery for neuroinflammation and neurodegeneration; Analyze and integrate multi-omics data to identify novel therapeutic targets, review and validate therapeutic hypotheses, better understand disease mechanisms and causal pathways, and inform translational medicine strategies; Apply machine learning to the rich multi-level data (genetics, multi-omics, neuroimaging, fluid biomarkers, and clinical data) for target discovery, indication prioritization, and biomarker development
Requirements: Ph.D. in Computational Biology, Neuroscience, Statistics, Computer Science, or a related discipline; 5+ years of relevant research experience in applying advanced computational biology to solving biomedical problems; Extensive experience in analyzing multi-omics data (e.g., genetics, bulk RNA-seq, single-cell RNA-seq, spatial omics, proteomics, etc.) for biomedical research; Strong background and knowledge in human biology and/or medicine, especially neuroscience and related diseases

Duties: Lead internal and external efforts to understand the pathogenesis of cardiovascular and metabolic disease (obesity, heart failure, atherosclerosis and vascular biology, chronic kidney disease and others) at a cellular and tissue level; Provide actionable biological insights and build scientific understanding of specific drug targets and biological pathways within the context of cardiovascular and metabolic diseases through the implementation and application of tools and methods to analyze omic/multi-omic data (including but not limited to scRNAseq, sc-multimomics, spatial transcriptomics, bulk RNAseq, epigenetics, and proteomics)
Requirements: Ph.D. in Biological Sciences, Molecular Biology, Biochemistry, Computational Biology, or related field required; 5+ years relevant experience applying quantitative approaches to generate and address biological questions, preferably in a pharmaceutical, biotech or comparable context; 3+ year’s experience in research of cardiovascular and metabolic diseases preferred, including but not limited to obesity, heart failure, atherosclerosis and vascular biology, chronic kidney disease

Duties: Development and implementation of advanced machine learning models specifically designed for analyzing heterogeneous biological data. This includes data from genomics, proteomics, metabolomics, and other biological sources; Advance the internal machine learning tech stack to cope with spurious correlation, obscuring variation and inherent multimodality of biomedical, chemical, and ‘omics data; Research, design and implement learning algorithms for analysis problems related to drug discovery; Be an active member of a highly interdisciplinary team; Conceive, execute and evaluate studies and experiments, interpret the results and present them to scientist in other functions
Requirements: Formal training in Biology, Computational Biology, Statistics, Machine Learning, or a related technical discipline; PhD and 2+ years of relevant research experience in developing machine/deep learning-based solutions and a sincere interest for computational life sciences; Hands-on experience in handling, processing, integrating, and analyzing large heterogenous biological data sets related to industrial drug discovery research (e.g., sc/snRNAseq, ATAC-seq, genomics, proteomics, etc.); Proven expertise in developing machine learning for computational biology