ROLE SUMMARY

Pfizer is making significant investment in the creation of novel, first-in-class therapeutics, by combining internal discovery and validation of drug targets with external partnerships to find the most innovative ideas, bring them to Pfizer R&D organization and move them to the clinic to deliver breakthroughs to patients.
This position is within Target Sciences (TS) team which works together with Pfizer’s alternative drug discovery model Centers for Therapeutic Innovation (CTI) within Pfizer’s broader Emerging Science & Innovation (ES&I) Unit. The primary responsibilities of this position are to lead computational biology efforts towards new and ongoing programs for ES&I portfolio through a combination of functional genomics, data mining and analysis of external and internal genetics and Omics data sets, and delivery of novel target hypotheses.
The Director will be accountable to and will work closely with lab-based biologists, computational biologists, and human geneticists to address key drug discovery questions on biological and disease mechanisms, biological rationale, target safety and patient stratification through the use of computational approaches to analyze and integrate in-house and public genetics, epigenetics, NGS and other Omics data types.
The Director will also work with other computational and statistics colleagues across the Integrative Biology groups within the Worldwide Research, Development, and Medical (WRDM) organization, Global Product Development (GPD), Real World Data (RWD), and Digital to leverage a broader suite of computational capabilities and analytical tools, lead the evaluation of cutting-edge tools to elucidate target and disease mechanisms, develop and test therapeutic hypotheses, identify disease biomarkers, and understand patient stratification.
The Director position requires deep expertise in computational biology, human genetics, drug development, and strong leadership skills with the ability to lead academic and industrial partnerships and consortia to support Target Sciences mission.
ROLE RESPONSIBILITIES

Lead quantitative analysis, data integration, and methodology development for computational projects by interacting with internal experts in statistics, bioinformatics, computational biology, and research project leaders. Lead internal and external collaborations that secure access to novel or proprietary data types and innovative computational methodologies to advance our ability to process and analyze novel biological data types and understand disease pathophysiology from large-scale molecular and phenotypic data.
Help build strong AI capabilities within ES&I linked into WRDM Biomedicine Artificial Intelligence Center of Excellence, leverage AI for new target identification, biomarker discovery, advancement of RNA Therapeutics platform, and contribute to due diligence and evaluations of new collaborations and opportunities.
Lead internal and external efforts to provide actionable biological insights and build scientific rationale for specific drug targets and biological pathways across different disease areas including Rare Disease, Oncology, Inflammation & Immunology, and Internal Medicine through the development, implementation and application of tools and methods to analyze genetic, NGS (including but not limited to RNAseq, scRNAseq, etc), and other Omics data types.
Support RNA Therapeutics project pipeline including target prioritization, computational support of in vitro and in vivo studies to inform formulation selection, sequence optimization, dosing paradigms and provide insights into platform optimization. Design and implement computational biology pipelines and algorithms in collaboration with other computational biology scientists and bioinformatics engineers to support RNA therapeutic development and research activities.
Communicate and report study findings and analyses with leadership, administration, and collaborators. Manage, support, and promote relationships with the external community that can add value to target selection at Pfizer and enable collaborative use of Pfizer patient samples Work collaboratively with other human geneticists and computational biologists at Pfizer.
BASIC QUALIFICATIONS

Ph.D. in Biological Sciences, Bioinformatics, Molecular Biology, Biochemistry, Computer Science, Applied Mathematics, or related field required; 9+ years relevant experience applying quantitative approaches to addressing biological questions, preferably in a pharmaceutical, biotech or comparable context.
4+ years industry experience working in matrixed cross-functional teams with demonstrated experience in design, execution, and interpretation of in vivo and/or in vitro biological experiments generating large scale molecular datasets, pathway analysis and NGS data types.
Fluency in start-to-end analysis of multiple types of Omics data such as bulk RNAseq, scRNAseq, spatial transcriptomics, ChIP-seq, proteomics, metabolomics, or epigenetics with particular expertise in at least one these. Sound statistical and quantitative skills with an understanding of the methods implemented by commonly used Omics data analysis packages.
Proficiency in artificial intelligence, machine/deep learning, and high-performance computing.
Extensive experience in bio-computational statistical programming languages (R) and working knowledge in one of the scripting/programing languages (Python/Java preferred) is essential.
Proven ability to build, lead, and manage teams and effectively interact with multidisciplinary scientists, researchers, and non-scientists.
Excellent communication, interpersonal, and presentation skills.
Track record of innovative and impactful research including peer-reviewed publications.
Strong desire and demonstrated ability to continually learn new skills.
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.
PREFERRED QUALIFICATIONS

Demonstrated ability for sound experimental design for in-silico experimentation/workflows required, in addition to ability to effectively interface with biologists to communicate/discuss results, hypotheses, and follow-up experiments.
In depth knowledge of relevant public and proprietary databases, methods, and tools. Good understanding of the statistical methods used in Omics analyses.
Familiarity with database management (SQL) a plus.
Experience with creating data visualization tools such as Shiny apps is a plus. Experience using application programming interfaces (APIs) also a plus.
Experience applying computational approaches to multi-dimensional datasets to deliver insights and hypotheses, e.g., multivariate, Bayesian and machine learning approaches.
Other Job Details

Eligible for Relocation Package

Eligible for Employee Referral Bonus