Data Scientist

Amgen's Global Quality Analytics and Innovation team is leading a digital transformation within the Quality organization. We are seeking a skilled Data Scientist with expertise in Generative AI, Large Language Models (LLMs), and MLOps, who also understands Quality in regulated environments (e.g., GxP). This role is crucial for designing, developing, and deploying scalable AI/ML solutions to enhance innovation, efficiency, and regulatory compliance.

You will collaborate with cross-functional teams, including software engineers, data engineers, business stakeholders, and quality professionals to deliver AI-driven capabilities aligned with strategic business objectives. The ideal candidate is an analytical problem-solver with strong technical acumen, excellent communication skills, and the ability to succeed in a fast-paced, agile setting.

Key Responsibilities

• Design, build, and deploy generative AI and LLM-based applications using frameworks like LangChain and LlamaIndex.
• Engineer effective prompts for LLMs such as OpenAI GPT-4 and Anthropic Claude.
• Develop and maintain evaluation metrics and frameworks for prompt engineering.
• Perform data quality assessments, data cleansing, and ingest unstructured documents into vector databases.
• Build retrieval algorithms to identify relevant data for LLMs and AI applications.
• Ensure AI/ML development adheres to GxP and other regulatory standards, promoting a strong Quality culture.
• Collaborate with global and local teams to support regulatory inspection readiness and future AI capabilities.
• Share insights and findings within an Agile (SAFe) environment.

Qualifications

Basic Qualifications:

• Master's degree and 2-4 years of experience in Software Engineering, Data Science, or ML Engineering; OR
• Bachelor's degree and 3-5 years of experience in Software Engineering, Data Science, or ML Engineering; OR
• Diploma and 7-9 years of experience in Software Engineering, Data Science, or ML Engineering

Preferred Qualifications:

• Experience in developing and deploying LLM applications.
• Strong understanding of ML algorithms, data science workflows, and NLP.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).
• Familiarity with MLOps tools (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 (Tableau, Streamlit, Dash).
• Knowledge of GxP compliance and experience in regulated environments.
• Excellent communication skills for explaining complex topics to diverse audiences.
• High initiative, self-motivation, and ability to work in global teams.