Career Category

Engineering

Job Description

Director Data Sciences

Role Name: Director Agentic AI

Department Name: AI & Data Science

ABOUT AMGEN

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 Director of Data Science - 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:

• Develop and execute a multi-year data-science strategy and roadmap that directly supports corporate objectives, translating it into measurable quarterly OKRs for the team.
• Lead, mentor and grow a high-performing staff of data scientists and ML engineers, providing technical direction, career development, and continuous‐learning opportunities.
• Own the end-to-end delivery of advanced analytics and machine-learning solutions—from problem framing and data acquisition through model deployment, monitoring and iterative improvement—ensuring each project delivers clear business value.
• Prioritise and manage a balanced portfolio of initiatives, applying ROI, risk and resource-capacity criteria to allocate effort effectively across research, clinical, manufacturing and commercial domains.
• Provide hands-on guidance on algorithm selection and experimentation (regression, classification, clustering, time-series, deep learning, generative-AI, causal inference), ensuring methodological rigour and reproducibility.
• Establish and enforce best practices for code quality, version control, MLOps pipelines, model governance and responsible-AI safeguards (privacy, fairness, explainability).
• Partner with Data Engineering, Product, IT Security and Business stakeholders to integrate models into production systems via robust APIs, dashboards or workflow automations with well-defined SLAs.
• Manage cloud and on-prem analytics environments, optimising performance, reliability and cost; negotiate vendor contracts and influence platform roadmaps where appropriate.
• Champion a data-driven culture by communicating insights and model performance to VP/SVP-level leaders through clear storytelling, visualisations and actionable recommendations.
• Track emerging techniques, regulatory trends and tooling in AI/ML; pilot innovations that keep the organisation at the forefront of data-science practice and compliance requirements.

Must-Have Skills:

• Leadership & Delivery: 10+ years in advanced analytics with 4+ years managing high-performing data-science or ML teams, steering projects from problem framing through production.
• Algorithmic Expertise: Deep command of classical ML, time-series, deep-learning (CNNs, transformers) and causal-inference techniques, with sound judgement on when and how to apply each.
• Production Engineering: Expert Python and strong SQL, plus hands-on experience deploying models via modern MLOps stacks (MLflow, Kubeflow, Sage Maker, Vertex AI or Azure ML) with automated monitoring and retraining.
• Business Influence: Proven ability to translate complex analytics into concise, outcome-oriented narratives that inform VP/SVP-level decisions and secure investment.
• Cloud & Cost Governance: Working knowledge of AWS, Azure or GCP, including performance tuning and cost-optimisation for large-scale data and GPU/CPU workloads.
• Responsible AI & Compliance: Familiarity with privacy, security and AI-governance frameworks (GDPR, HIPAA, GxP, EU AI Act) and a track record embedding fairness, explainability and audit controls throughout the model lifecycle.

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 and or Data Science
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications

• Master’s degree with 15 - 17 + years of experience in Computer Science, IT or related field OR
• Bachelor’s degree with 18 - 20 + 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.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.

We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

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