ROLE SUMMARY
The Senior Scientist, Sterile Injectable Design (SID) Predictive Sciences is responsible for Predictive Science activities supporting end-to-end SI drug product design. To deliver significant acceleration opportunities for our patients and the organization, an integrated and holistic approach within SID and PGS is expected. The role requires the candidate to be able to effectively work not only with direct reports, but also subject matter experts within their areas of expertise. The candidate must be able to translate the needs of subject matter experts within respective SID and Manufacturing lines to ensure plans, implementations, and work products are of highest quality and meet the needs of the business lines. He/She should work with colleagues in Chennai/India/Global.
ROLE RESPONSIBILITIES
The Senior Scientist, SID Predictive Sciences provides leadership, guidance, and alignment for the development and deployment of end-to-end Predictive Sciences and the adoption and implementation of these predictive tools within early and late-stage programs to effectively support the SID / PGS business strategy.
The incumbent is responsible for the following key activities:
• Lead Molecular Dynamics (MD) and data-driven modeling initiatives within GH&B-GTEL SID Predictive Sciences, integrating physics-based simulations, machine learning, and digital workflows across Digital Design, Digital Lab, Digital Manufacturing, and Data Core platforms.
• Partner with Pfizer scientists to build mechanistic understanding of molecular interactions, material properties, stability, and formulation behavior across SI dosage forms, applying MD, data models, and AI/ML to support formulation design, optimization, and troubleshooting.
• Integrate simulation outputs with data engineering frameworks, including trajectory data, feature extraction pipelines, scalable data models, vector databases, and embeddings for molecular similarity, knowledge retrieval, and decision support.
• Collaborate with Sterile Injectable Design teams across development stages to apply molecular insights and AI-driven applications to formulation selection, process design, stability risk assessment, and technology transfer, while communicating technical and business risks to senior management.
• Apply Generative AI (GenAI) to automate simulation setup, analyze MD outputs, generate reports, and extract insights from scientific literature to accelerate and improve decision-making.
• Partner with global colleagues and digital leaders to drive adoption of MD, AI/ML, and GenAI-enabled tools in SI product development, advancing digital-first and model-informed workflows.
• Lead development and deployment of advanced simulation and ML capabilities, including automated pipelines, hybrid models, and scalable HPC/cloud workflows, and present outcomes through reports, visualizations, and internal and external presentations.
• Collaborate with external partners, including academia, research organizations, and software vendors, to deliver advanced modeling, ML, and GenAI initiatives aligned with organizational priorities.
• Define short- and long-term objectives for MD, ML, and GenAI initiatives within assigned projects, aligned with SID digital transformation goals and portfolio needs.
• Establish frameworks to expand adoption of molecular modeling, AI/ML, and GenAI across sterile injectables workflows, partnering with leadership to secure resources and integrate digital accelerators across end-to-end product development.
BASIC QUALIFICATIONS
Ph.D. in Computational Chemistry, Chemical Engineering, Pharmaceutical Engineering, Physics, Materials Science, or related discipline, with strong foundation in classical molecular dynamics and computational modeling.
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Strong interest and hands-on experience in classical MD simulations and multiscale molecular modeling, including all-atom MD, coarse-grained MD (CGMD), ab initio methods, and mesoscale techniques such as Dissipative Particle Dynamics (DPD), with 2–3 years of relevant industrial or research experience (preferably pharma or related industries).
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Hands-on experience with industry-standard simulation packages such as GROMACS, OpenMM, LAMMPS, NAMD, along with quantum chemistry tools like Gaussian and ORCA, and integrated platforms such as Materials Studio and Schrödinger Suite.
Hands-on experience with industry-standard simulation packages such as GROMACS, OpenMM, LAMMPS, NAMD, along with quantum chemistry tools like Gaussian and ORCA, and integrated platforms such as Materials Studio and Schrödinger Suite.
Hands-on experience with industry-standard simulation packages such as GROMACS, OpenMM, LAMMPS, NAMD, along with quantum chemistry tools like Gaussian and ORCA, and integrated platforms such as Materials Studio and Schrödinger Suite.
Strong experience with HPC environments, GPU acceleration, and cloud computing platforms, including performance optimization and large-scale simulation execution.
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Hands-on experience with Python-based ML/AI ecosystem (NumPy, SciPy, Pandas, Scikit-learn, PyTorch, TensorFlow), building predictive models from simulation data, with exposure to applied GenAI for scientific workflows and insight generation.
Hands-on experience with Python-based ML/AI ecosystem (NumPy, SciPy, Pandas, Scikit-learn, PyTorch, TensorFlow), building predictive models from simulation data, with exposure to applied GenAI for scientific workflows and insight generation.
Hands-on experience with Python-based ML/AI ecosystem (NumPy, SciPy, Pandas, Scikit-learn, PyTorch, TensorFlow), building predictive models from simulation data, with exposure to applied GenAI for scientific workflows and insight generation.
Hands-on experience with Python-based ML/AI ecosystem (NumPy, SciPy, Pandas, Scikit-learn, PyTorch, TensorFlow), building predictive models from simulation data, with exposure to applied GenAI for scientific workflows and insight generation.
Excellent communication, technical writing, and collaboration skills, with ability to translate complex simulation and AI outputs into actionable insights.
undefined· Fluent in English.
Work Location Assignment: On Premise
Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.

Research and Development
