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Strong employer brand, mid/senior generalist ML role, and metro location increase applicant competition.
Core ML/GenAI skills are transferable, though pharma/life-sciences familiarity is preferred rather than mandatory.
Explicit 6–12 years plus deep ML/GenAI, cloud and validation requirements create strict shortlisting filters.
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Build and model AI/ML and GenAI digital products and platforms to accelerate discovery, manufacturing, commercial analytics, and corporate functions.
Collaborate with technical architects, product managers, UX designers, and back-end engineers to design secure, scalable, user-centric AI/ML solutions.
Embed responsible-AI and security-by-design controls while experimenting with large language models and generative AI technologies.
6–12 years of experience in Data Science including managing structured and unstructured data and applying statistical techniques.
Degree required: Doctorate, Master's, or Bachelor's in Computer Science, Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, or related field.
Strong expertise in deep learning, machine learning, NLP, and data mining with experience in Python packages (PyTorch, TensorFlow, Hugging Face, Scikit-learn, etc.) and programming languages (C, C++, Java, CUDA, SQL, NoSQL).
Experience with cloud platforms, preferably AWS, to build scalable AI/ML solutions.
Experienced in developing cloud-scale AI/ML systems and working with open-source data science stacks.
Capable of collaborating effectively across cross-functional teams including architects, SMEs, and engineers to deliver fast-paced business solutions.
Familiar with pharma/life sciences AI environments and knowledgeable about Responsible AI and model validation principles.