Director - Agentic AI

Director Data Sciences

Role Name: Director Agentic AI

Department Name: AI & Data Science

Role Description:

We are seeking a Director of Data Science - Amgens 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 modelsclassical ML, deep learning and LLMssecurely 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 continuouslearning opportunities. Own the end-to-end delivery of advanced analytics and machine-learning solutionsfrom problem framing and data acquisition through model deployment, monitoring and iterative improvementensuring 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, SageMaker, 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.
• 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 15 - 17 + years of experience in Computer Science, IT or related fieldOR
• Bachelors 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.