The Global Quality Analytics and Innovation team leads the digital transformation and innovation effort throughout Amgen’s Quality organization. We are at the forefront of developing and rolling out data-centric digital tools, employing automation, artificial intelligence (AI), and generative AI to drive end-to-end quality transformation. We are seeking a highly motivated and experienced Data Scientist with a strong background in Generative AI, Large Language Models (LLMs), and MLOps, along with an understanding for Quality in regulated environments (e.g., GxP). This role will play a key part in designing, developing, and deploying scalable AI/ML solutions to drive innovation, efficiency, and regulatory compliance across the organization.
You will collaborate with cross-functional teams, including software engineers, data engineers, business stakeholders, and quality professionals to deliver AI-driven capabilities that support strategic business objectives. The ideal candidate is an analytical thinker with excellent technical depth, communication skills, and the ability to thrive in a fast-paced, agile environment.
Key Responsibilities
- Design, build, and deploy generative AI and LLM-based applications using frameworks such as LangChain, LlamaIndex, and others.
- Engineer reusable and effective prompts for LLMs like OpenAI GPT-4, Anthropic Claude, etc.
- Develop and maintain evaluation metrics and frameworks for prompt engineering.
- Conduct data quality assessments, data cleansing, and ingestion of unstructured documents into vector databases.
- Build retrieval algorithms for relevant data identification to support LLMs and AI applications.
- Ensure AI/ML development complies with GxP and other regulatory standards, fostering a strong Quality culture.
- Partner with global and local teams to support regulatory inspection readiness and future technological capabilities in AI.
- Share insights and findings with team members in an Agile (SAFe) environment.
Preferred Qualifications
- Master’s degree and 2–4 years of experience in Software Engineering, Data Science, or ML Engineering
- Experience in developing and deploying LLM applications.
- Strong foundation in ML algorithms, data science workflows, and NLP.
- Expertise in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
- Familiarity with MLOps tools (e.g., MLflow, CI/CD, version control).
- Experience with cloud platforms (AWS, Azure, GCP) and tools like Spark, Databricks.
- Understanding of RESTful APIs and frameworks like FastAPI.
- Experience with BI and visualization tools (e.g., Tableau, Streamlit, Dash).
- Knowledge of GxP compliance and experience working in regulated environments.
- Strong communication skills with the ability to explain complex topics to diverse audiences.
- High degree of initiative, self-motivation, and ability to work in global teams.
