Director of Scientific Computing and AI Platforms

Director, Scientific Compute & AI Platforms

Career Category

Information Systems

Job Description

You will lead the AIN-based data science organization that builds and operates scientific computational, data, and AI/ML pipelines and workflows for Amgen Research. You will operate across India/EU/US (Eastern, Central, Pacific) time zones and coordinate outcomes across the ARIA, Global Research, ATMOS Tech, and ATMOS AI&D. You will own portfolio outcomes across the Research data ecosystem, AI/ML for research, and high-performance computing enablement, ensuring robust, resilient pipelines and reliable, scalable services.

Core Responsibilities

• Set multi-year strategy, organization design, and investment roadmap for research compute & AI platforms.
• Establish standards and promote methods for robust, resilient computational and data science pipelines; drive adoption and measure cycle-time impact.
• Govern capacity, cost, and quality: quotas, GPU utilization, $/compute-hour, and SLA attainment.
• Partner on schemas, vocabularies, lineage, and compliance with Reseach data stewards.
• Coordinate instrument onboarding and lab software integrations with RA&T.
• Mentor and grow L5/L4 engineers; develop on-call rotations and technical leadership bench.

Core Competencies

• Enterprise strategy, product/platform thinking, and financial acumen.
• Global change leadership; cross-cultural communication across time zones.
• Platform engineering literacy: containers/Kubernetes; workflow orchestration (Nextflow/Airflow/Argo/Prefect).
• Compute literacy: HPC schedulers (Slurm) and/or distributed compute (Spark/Ray/Databricks).
• Data governance literacy: schemas, vocabularies, lineage, and compliance.
• Executive communication; stakeholder management across science, engineering, and operations.

Qualifications

• Basic: Bachelor’s degree required; advanced degree (MSc/PhD) preferred
• Scientific literacy: Training and experience in chemistry, biology, or related scientific fields
• Domain literacy: Familiarity with R&D domains (biology, chemistry, protein science, computational sciences) and experience partnering closely with scientists.
• Technical literacy: Python plus one of Java/Scala/C++; containers/Kubernetes; workflow orchestration; HPC schedulers; distributed compute; data lakehouse; CI/CD
• Experience: 18–24 years in platform/software/data leadership with cross‑region operations; budget/vendor management; policy/standards influence

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Experience Level

Executive Level