Penn Foster
Senior Machine Learning Research Scientist
Penn Foster, Cambridge, Massachusetts, us, 02140
ROLE SUMMARY
Pfizer Machine Learning Computational Sciences (MLCS) group has an opening for a computational methods developer with expertise in molecular modeling, Artificial Intelligence (AI), Machine Learning (ML), and scientific programming. The successful candidate will identify novel and creative applications of AI/ML and develop cutting-edge predictive and interpretable models to advance discovery and development efforts across Pfizer. This is an exciting opportunity to join a growing group of computational scientists and machine learning researchers who are passionate about developing novel computational methods and ML models to address challenging problems from early discovery to late-stage development and across established (small molecule and antibody) and emerging (mRNA therapeutics and gene therapy) therapeutic modalities.
ROLE RESPONSIBILITIES
Apply and extend the latest deep learning-based structure prediction methods to model T-cell receptors (TCR) and TCR-peptide-MHC complexes, supporting an interdisciplinary effort to explore potential therapeutic applications of TCRs.
Collaborate with colleagues from diverse scientific backgrounds to identify problems and opportunities; combine techniques from computational chemistry, computational biology, and AI/ML, particularly utilizing recent deep learning techniques, to rapidly develop powerful computational solutions.
Effectively utilize relevant public and proprietary databases and available computational resources (internal HPC and Cloud) to develop predictive models to assess pharmacological and developability properties of candidate molecules from different therapeutic modalities (small molecules, antibodies, mRNA etc).
Leverage proprietary computational framework and applications to deploy ML models and other tools for wide usage by Pfizer scientists.
Communicate and explain computational models and ML algorithms to a broad scientific audience from diverse disciplines.
Remain current with relevant scientific literature; proactively identify, assess, and internalize promising methods and tools from external sources.
Strengthen Pfizer’s external visibility and scientific reputation of excellence through publications in high-impact scientific journals and presentations at external conferences.
BASIC QUALIFICATIONS
Ph.D. in computational chemistry, computational biology, physical or biological sciences, chemical engineering, computer science, or related discipline.
Proficiency in Python; experience with scientific programming and algorithm design related to machine learning.
Practical hands-on experience with developing predictive models using modern deep learning techniques (e.g., CNNs and transformers) and packages (e.g., PyTorch, TensorFlow, JAX).
Track record of applying machine learning, in particular modern deep learning approaches, to solve relevant biological problems.
Proficiency in general molecular modeling techniques and familiarity with concepts, techniques, and common tools used for sequence analysis and protein structure modeling.
Experience with Unix/Linux and HPC environments.
Excellent communication and interpersonal skills.
PREFERRED QUALIFICATIONS
Strong publication record and demonstrated contribution to the machine learning field, e.g., NeurIPS, ICML, ICLR, etc.
Demonstrated track record of applying several AI/ML techniques such as ConvNet, transformers, generative modeling, and reinforcement learning to tackle complex drug discovery and development problems.
Experience in applying ML to immunology problems such as modeling of HLA-peptide and HLA-peptide-TCR structure and binding.
Additional Information:
Relocation Support Available
Eligible for employee referral
Work Location Assignment: On Premise
On Premise colleagues work in a Pfizer site because it’s needed to get their job done. They may have flexibility to work remotely from time to time, but they are primarily on-site.
Last Day to Apply: August 18, 2023
#J-18808-Ljbffr
Pfizer Machine Learning Computational Sciences (MLCS) group has an opening for a computational methods developer with expertise in molecular modeling, Artificial Intelligence (AI), Machine Learning (ML), and scientific programming. The successful candidate will identify novel and creative applications of AI/ML and develop cutting-edge predictive and interpretable models to advance discovery and development efforts across Pfizer. This is an exciting opportunity to join a growing group of computational scientists and machine learning researchers who are passionate about developing novel computational methods and ML models to address challenging problems from early discovery to late-stage development and across established (small molecule and antibody) and emerging (mRNA therapeutics and gene therapy) therapeutic modalities.
ROLE RESPONSIBILITIES
Apply and extend the latest deep learning-based structure prediction methods to model T-cell receptors (TCR) and TCR-peptide-MHC complexes, supporting an interdisciplinary effort to explore potential therapeutic applications of TCRs.
Collaborate with colleagues from diverse scientific backgrounds to identify problems and opportunities; combine techniques from computational chemistry, computational biology, and AI/ML, particularly utilizing recent deep learning techniques, to rapidly develop powerful computational solutions.
Effectively utilize relevant public and proprietary databases and available computational resources (internal HPC and Cloud) to develop predictive models to assess pharmacological and developability properties of candidate molecules from different therapeutic modalities (small molecules, antibodies, mRNA etc).
Leverage proprietary computational framework and applications to deploy ML models and other tools for wide usage by Pfizer scientists.
Communicate and explain computational models and ML algorithms to a broad scientific audience from diverse disciplines.
Remain current with relevant scientific literature; proactively identify, assess, and internalize promising methods and tools from external sources.
Strengthen Pfizer’s external visibility and scientific reputation of excellence through publications in high-impact scientific journals and presentations at external conferences.
BASIC QUALIFICATIONS
Ph.D. in computational chemistry, computational biology, physical or biological sciences, chemical engineering, computer science, or related discipline.
Proficiency in Python; experience with scientific programming and algorithm design related to machine learning.
Practical hands-on experience with developing predictive models using modern deep learning techniques (e.g., CNNs and transformers) and packages (e.g., PyTorch, TensorFlow, JAX).
Track record of applying machine learning, in particular modern deep learning approaches, to solve relevant biological problems.
Proficiency in general molecular modeling techniques and familiarity with concepts, techniques, and common tools used for sequence analysis and protein structure modeling.
Experience with Unix/Linux and HPC environments.
Excellent communication and interpersonal skills.
PREFERRED QUALIFICATIONS
Strong publication record and demonstrated contribution to the machine learning field, e.g., NeurIPS, ICML, ICLR, etc.
Demonstrated track record of applying several AI/ML techniques such as ConvNet, transformers, generative modeling, and reinforcement learning to tackle complex drug discovery and development problems.
Experience in applying ML to immunology problems such as modeling of HLA-peptide and HLA-peptide-TCR structure and binding.
Additional Information:
Relocation Support Available
Eligible for employee referral
Work Location Assignment: On Premise
On Premise colleagues work in a Pfizer site because it’s needed to get their job done. They may have flexibility to work remotely from time to time, but they are primarily on-site.
Last Day to Apply: August 18, 2023
#J-18808-Ljbffr