Principal Data Scientist

Roles & Responsibilities:

• Define and evangelise the multi-year AI-platform vision, architecture blueprints and reference implementations that align with Amgen s digital-transformation and cloud-modernization objectives.
• Design and evolve foundational platform components feature stores, model-registry, experiment-tracking, vector databases, real-time inference gateways and evaluation harnesses using cloud-agnostic, micro-service principles.
• Implement robust MLOps pipelines (CI/CD for models, automated testing, canary releases, rollback) and enforce reproducibility from data ingestion to model serving.
• Embed responsible-AI and security-by-design controls data-privacy, lineage tracking, bias monitoring, audit logging through policy-as-code and automated guardrails.
• Serve as the ultimate technical advisor to product squads: codify best practices, review architecture/PRs, troubleshoot performance bottlenecks and guide optimisation of cloud resources.
• Partner with Procurement and Finance to develop TCO models, negotiate enterprise contracts for cloud/AI infrastructure, and continuously optimise spend.
• Drive platform adoption via self-service tools, documentation, SDKs and internal workshops; measure success through developer NPS, time-to-deploy and model uptime SLAs.
• Establish observability frameworks metrics, distributed tracing, drift detection to ensure models remain performant, reliable and compliant in production.
• Track emerging technologies (serverless GPUs, AI accelerators, confidential compute, policy frameworks like EU AI Act) and proactively integrate innovations that keep Amgen at the forefront of enterprise AI.

Must-Have Skills:

• 5-7 years

  in AI/ML, data platforms or enterprise software, including

  3+ years

  leading senior ICs or managers.
• Proven track record selecting and integrating AI SaaS/PaaS offerings

  and

  building custom ML services at scale.
• Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
• Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
• Strong business-case skills able to model TCO vs. NPV and present trade-offs to executives.
• Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.

Good-to-Have Skills:

• Experience in Biotechnology or pharma industry is a big plus
• Published thought-leadership or conference talks on enterprise GenAI adoption.
• Master s degree in Computer Science, Data Science or MBA with AI focus.
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications

• Master s degree with 10-14 + years of experience in Computer Science, IT or related field

OR

• Bachelor s degree with 12-17 + years of experience in Computer Science, IT or related field
• Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.

Soft Skills:

• Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills
• Ability to work effectively with global, virtual teams
• High degree of initiative and self-motivation.
• Ability to manage multiple priorities successfully.
• Team-oriented, with a focus on achieving team goals.
• Ability to learn quickly, be organized and detail oriented.
• Strong presentation and public speaking skills.