Sr Associate - Commercial Analytics & AI Enablement (Data Scientist)

Career Category Operations Job Description Amgen India (AIN) The Commercial Analytics team at Amgen is a strategic partner to Commercial leadership. It delivers high-quality analyses and data-backed insights that drive US commercial performance. This role is part of Amgen's growing analytics capability in India. It works closely with US-based Commercial and Global Customer Capabilities Innovation teams to bring AI into how we generate insights and support decisions across the business. As our Commercial Analytics Sr. Associate for AI Innovation & Enablement, you will play a hands-on role in turning AI ideas into working, trusted solutions across the brand portfolio. You will partner with the Manager to carry out the team's AI agenda. That means building and scaling AI use cases, shaping reusable tools and platforms, and making sure every AI output meets a high bar for accuracy and compliance. The role calls for curiosity, strong learning agility, close attention to detail, and a builder's mindset. Live | What you will do 1. AI Use Case Delivery & Innovation Partner with the Manager to find, prioritize and deliver AI use cases across the commercial portfolio, including performance analytics, insight generation, reporting automation, market research and competitive intelligence. Build and iterate on AI-enabled solutions using generative AI, LLMs, coding assistants and agentic workflows, from proof-of-concept through production. Turn business problems into clear AI solution designs, covering inputs, logic, outputs, success criteria and value measures. Keep up with new AI tools, methods and industry practices, and bring practical ideas back to the team. 2. Platform Build & Productization Help build reusable AI assets such as prompt libraries, templates, agents, code modules and workflow accelerators that scale across brands and teams. Connect AI solutions to governed commercial data sources such as claims, specialty pharmacy, CRM and secondary data, working with data engineering and platform teams. Document solutions, data lineage, business rules and caveats so outputs are repeatable, auditable and easy to hand over. Help move successful pilots into standard, scalable capabilities that cut manual effort and speed up insights. 3. AI Quality, Governance & Responsible Use Design and apply quality frameworks for AI outputs, including validation checks, evaluation criteria, benchmark datasets, human-in-the-loop review and error tracking. Make sure AI-generated insights are accurate, reproducible and traceable to source data before they reach business stakeholders. Follow Amgen's responsible AI, data privacy, legal, regulatory and compliance SOPs across internal teams and vendor partners. Monitor solution performance after deployment and drive continuous improvement. 4. Adoption, Enablement & Cross-Functional Collaboration Drive AI adoption across the analytics team through demos, playbooks, training sessions, office hours and knowledge-sharing forums. Work closely with US teams across brand analytics, forecasting, market research, value and access, decision science, IS/technology, and reporting and visualization. Gather requirements, test assumptions and collect user feedback to refine solutions and grow adoption. Track and report on adoption, time saved and business impact of AI initiatives. Model a culture of curiosity, experimentation, accountability, inclusion and continuous learning across the India team. Thrive | What you can expect As we work to develop treatments that take care of others, we also work to care for our teammates' professional and personal growth and well-being. Basic Qualifications Bachelor's degree with 3–6 years, or Master's degree with 2–4 years, of experience in analytics, data science or sales and marketing operations. Strong Python, JS, AI Development skills, plus hands-on experience building solutions with generative AI or LLMs, such as prompt engineering, API integration, retrieval-augmented generation (RAG) or agentic workflows. Working knowledge of cloud data platforms (AWS, Databricks) and visualization tools such as Tableau. Experience working with structured commercial or healthcare data, ideally US commercial pharma secondary data. Preferred Qualifications Proven track record of taking an analytics or AI solution from idea to production with real user adoption. Experience designing validation, testing or quality-assurance frameworks for analytical or AI outputs. Familiarity with AI coding assistants and enterprise AI platforms, and with version control and engineering best practices (Git, code review, documentation). Understanding of the strengths, limitations and risks of different AI and analytical approaches, including hallucination, bias and data leakage. High learning agility: quick to pick up new tools and domains and to apply them to business problems. Strong attention to detail and ownership of delivery across cross-functional, multi-geography teams. Strong verbal and written communication skills, with the ability to explain technical solutions as a short, clear business story to non-technical audiences .