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Tier-1 employer, metro location, and broad attractive ML/GenAI role increase applicant density.
Core ML, NLP, and engineering skills are highly transferable across industries despite pharma domain knowledge preference.
Explicit 1–3 years, mandatory 2–3 projects, and concrete ML/GenAI tech requirements increase filtering rigor.
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Develop and maintain production-grade AI/ML platforms impacting US Value & Access commercial strategies, including AI copilots, intelligent contract analysis, and anomaly detection systems.
Collaborate with data scientists, engineers, and business partners to integrate AI/ML solutions using GenAI, LLMs, and machine learning from prototype through production.
Contribute to scalable data pipelines, backend services, and AI-driven analytics to support real-time decision-making and commercial optimization.
Bachelor's or Master's degree in Computer Science, AI, Data Science, Statistics, Engineering, or related STEM field.
1-3 years of hands-on experience in AI/ML, data science, or software engineering roles with a demonstrated portfolio of AI/ML projects.
Strong programming skills in Python, PySpark, SQL; solid understanding of data structures, algorithms, OOP, and system design.
Mandatory experience with core ML techniques, NLP fundamentals, exposure to LLMs and prompt engineering, and working with structured and unstructured data.
Experience executing AI/ML projects that progressed from prototype to production, especially involving GenAI and large language models.
Ability to work cross-functionally in agile teams, collaborating with business and technical stakeholders to translate requirements into AI solutions.
Familiarity with pharmaceutical or healthcare data ecosystems, and practical engineering experience with MLOps, REST APIs, and AI system deployment.