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Strong global brand and metro location increase competition, while ML specialization narrows the pool.
Core ML/AI skills transfer across industries, though pharma domain knowledge moderately increases fit sensitivity.
Explicit 6+ years and required ML/MLOps stack make screening filters strict.
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Develop and deliver AI-powered and machine learning solutions to accelerate availability of medicines and drive business impact in Global Market Access and HEOR IT.
Design, build, and scale ML pipelines that incorporate new modeling approaches and operationalize AI solutions in partnership with business and IT stakeholders.
Act as a knowledge expert on foundation models and emerging AI trends, communicating value and driving adoption across technical and non-technical audiences.
6+ years of experience in data analytics, machine learning, or related field.
Proficient in Python programming and frameworks such as PyTorch or TensorFlow; experience with generative AI, prompt engineering, and vector databases.
Familiarity with cloud computing platforms (AWS/GCP/Azure) and MLOps principles including model development lifecycle and governance.
Work Experience Required: 6+ years relevant experience. Notice period: Not explicitly mentioned in the JD.
Experienced in developing and operationalizing advanced AI/ML solutions, including foundation models and generative AI, within large or complex organizations.
Ability to translate complex data science concepts into actionable insights for diverse stakeholders, influencing strategic business decisions.
Comfortable leading initiatives involving new technologies and driving process improvements in data engineering and AI deployments.