Principal Data Scientist

Amgen harnesses the best of biology and technology to fight the world s toughest diseases, and make people s lives easier, fuller and longer. we'discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what s known today.

ABOUT THE ROLE

Role Description:

We are seeking a Principal AI Platform Architect Amgen s most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI platforms. Sitting at the intersection of engineering excellence and data-science enablement, you will design the core services, infrastructure and governance controls that allow hundreds of practitioners to prototype, deploy and monitor models classical ML, deep learning and LLMs securely and cost-effectively. Acting as a player-coach, you will establish platform strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI developer experience.

Roles Responsibilities:

Define and evangelise a multi-year AI-platform vision and reference architecture that advances Amgen s digital-transformation, cloud-modernisation and product-delivery 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.

Establish modelling and algorithm-selection standards that span classical ML, tree-based ensembles, clustering, time-series, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques; advise product squads on choosing and operationalising the right algorithm for each use-case.
Orchestrate the full delivery pipeline for AI solutions pilot regulated validation production rollout post-launch monitoring defining stage-gates, documentation and sign-off criteria that meet GxP/CSV and global privacy requirements.
Scale AI workloads globally by engineering autoscaling GPU/CPU clusters, distributed training, low-latency inference and cost-aware load-balancing, maintaining Implement robust MLOps and release-management practices (CI/CD for models, blue-green canary deployments, automated rollback) to ensure zero-downtime releases and auditable traceability.
Embed responsible-AI and security-by-design controls data privacy, lineage tracking, bias monitoring, audit logging through policy-as-code and automated guardrails.
Package reusable solution blueprints and APIs that enable product teams to consume AI capabilities consistently, cutting time-to-production by 50 %.
Provide deep technical mentorship and architecture reviews to product squads, troubleshooting performance bottlenecks and guiding optimisation of cloud resources.
Develop TCO models and FinOps practices, negotiate enterprise contracts for cloud/AI infrastructure and deliver continuous cost-efficiency improvements.
Establish observability frameworks metrics, distributed tracing, drift detection, SLA dashboards to keep models performant, reliable and compliant at scale.
Track emerging technologies and regulations (serverless GPUs, confidential compute, EU AI Act) and integrate innovations that maintain Amgen s leadership in enterprise AI.

Must-Have Skills:

• 5-7 years in AI/ML, data platforms or enterprise software.
• Comprehensive command of machine-learning algorithms regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques with the judgment to choose, tune and operationalise the right method for a given business problem.
• 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 (eg, 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.
• masters degree in Computer Science and or Data Science
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications

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

OR

• bachelors 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.