Sr Associate Data Scientist

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.