Use Your Power for Purpose
At Pfizer our purpose is to deliver breakthroughs that transform patients lives. Central to this mission is our Research and Development team which strives to convert advanced science and cutting-edge technologies into impactful therapies and vaccines. Whether you are engaged in discovery sciences ensuring drug safety and efficacy or supporting clinical trials your role is crucial. You will leverage innovative design and process development capabilities to expedite the delivery of top-tier medicines to patients globally.
What You Will Achieve
In this role work ing at the interface of viral genomics antigenicity modeling evolutionary forecasting and deep learning you will design implement and validate AI-driven models for prospective vaccine strain selection . More specifically you will:
Develop sequence-based deep learning models for rapidly evolving virus including:
transformer or language-model-based architectures for viral protein sequences and
graph neural networks that predict time-dependent changes in strain dominance .
Integrate multi-source surveillance immunogenicity and vaccine efficacy data to compute and evaluate prospective coverage scores for candidate vaccine strains .
Utilize interpretation frameworks to identify key feature s for virus evolutional advantage related to infectious disease burden and vaccine antigen design .
Conduct rigorous retrospective and prospective benchmarking validation . iterative fine-tuning to improve model performance
Communicate complex data and results clearly to both technical and non-technical stakeholders. Collaborate extensively with those from other scientific disciplines within the group from other subdivisions of Pfizer and potentially from external partner s.
Publish impactful scientific findings while safeguarding confidential data ensuring clear transparent reporting of methods and results to facilitate reproducibility and recognition in peer-reviewed journals and conferences.
Minimum Requirements
Ph.D. in Computational Biology Bioinformatics Computer Science M achine L earning or a closely related field.
Demonstrated ability to independently design and implement complex ML models evidenced by first author publications or equivalent open-source research contributions.
Strong hands-on experience with deep learning for sequence data including
transformer or language-model architectures and
model training validation and benchmarking on large biological datasets
Proficiency in Python and modern ML frameworks (e.g. PyTorch TensorFlow scikit-learn) with e xperience managing full modelling pipelines and statistical modeling including regression analysis and mixed-effects models.
Experience working with viral or microbial sequence data including alignment curation and longitudinal analysis across time.
Less than 2 years of post-degree experience.
Two letters of recommendation must be provided prior to interview .
Willingness to make a minimum 2-year commitment.
Strong communication and collaboration skills with the ability to work effectively in a hybrid team environment. Strong organizational skills and attention to detail in managing deadlines and documentation.
Ability to clearly communicate complex modeling concepts and results to both technical and biological audiences.
Preferred Qualifications
Direct experience with viral evolution modeling fitness/dominance prediction or time-resolved sequence forecasting.
Experience building or extending protein language models or MSA-based neural networks for biological inference.
Familiarity with antigenicity data or related experimental measurements and how such data can be integrated into machine learning models
Knowledge of SHAP or similar model interpretation frameworks for feature attribution in complex models.
Prior work on influenza SARS CoV 2 or other rapidly evolving viruses particularly in the context of immune escape antigenic drift or vaccine design.
Additional Information
Relocation support available
Location: On premise
Relocation assistance may be available based on business needs and/or eligibility.
Candidates must be authorized to be employed in the U.S. by any employer.
U.S. work visa sponsorship (such as TN O-1 H-1B etc.) is not available for this role now or in the future.
Sunshine Act
Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care providers name address and the type of payments or other value received generally for public disclosure. Subject to further legal review and statutory or regulatory clarification which Pfizer intends to pursue reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse your name address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter please do not hesitate to contact your Talent Acquisition representative.
EEO & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race color religion sex sexual orientation age gender identity or gender expression national origin disability or veteran status. Pfizer also complies with all applicable national state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer. This position requires permanent work authorization in the United States.
Pfizer endeavors to make to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing please email . This is to be used solely for accommodation requests with respect to the accessibility of our website online application process and/or interviewing. Requests for any other reason will not be returned.
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