Associate Data Scientist - Value & Access Strategy
Amgen (company site, opens in a new tab)2–6 yrsHyderabadOn-siteDirect career pageEst. 16–34 LPA
Posted 12h ago
Reach people at Amgen
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Amgen you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop and maintain production-grade AI and ML systems including AI copilots, document AI, data pipelines, and real-time decision platforms impacting commercial and patient access strategies.
- Collaborate with senior data scientists, engineers, and business stakeholders to translate business needs into scalable AI-driven automation and insights.
- Contribute to design, code reviews, and deployment best practices focusing on GenAI, LLM, NLP, and anomaly detection models with measurable business impact.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science, AI, Data Science, Statistics, Engineering, or related STEM field.
- 2–6 years of hands-on experience in AI/ML, data science, or software engineering roles.
- Strong programming skills in Python, PySpark, SQL, and solid understanding of ML fundamentals including NLP and LLM exposure.
- Mandatory hands-on experience with 2–3 real AI/ML projects involving LLM-based chatbots, AI summarization tools, agent systems, or deployed ML models and data pipelines.
Ideal Candidate Profile
- Experienced in building scalable AI/ML solutions from prototype to production with a focus on GenAI, LLM, and NLP in a commercial or healthcare context.
- Comfortable working in cross-functional technical teams translating complex business requirements into AI-powered products.
- Familiar with AI infrastructure, MLOps best practices, and integrating structured and unstructured data in production environments.