Senior Manager, Data Science, AI and Advance Analytics Commercial & Medical

Major Responsibilities:
Analytics & AI Development

Serve as a hands‑on data science practitioner, actively developing models, writing code, conducting exploratory data analysis, engineering features, validating outputs, and supporting deployment of AI/ML solutions into business workflows.

• Develop, validate, and deploy machine learning, generative AI, and advanced analytics solutions to address Commercial and Medical business challenges, including rare disease patient identification, HCP/HCO segmentation, forecasting, customer analytics, patient journey analytics, and insight generation.

• Translate complex business problems into well-defined data science use cases, technical requirements, analytical approaches, and implementation plans.

• Build, evaluate, and monitor analytical models and data pipelines, ensuring reproducibility, scalability, explainability, and alignment with CSL data governance standards.

• Support the design and implementation of generative AI and LLM‑based solutions, including retrieval‑augmented generation, knowledge mining, conversational analytics, and agentic workflows, as applicable.

• Apply appropriate model validation, monitoring, drift detection, and performance measurement approaches to ensure AI/ML solutions remain accurate, reliable, and fit for purpose.

• Partner closely with Commercial, Medical Affairs, Market Access, I&T, Data Governance, Compliance, and other enterprise partners to ensure data science and AI solutions are aligned with strategic business needs and enterprise standards.
• Serve as a bridge between business stakeholders and technical teams by translating business needs into actionable analytical requirements and communicating technical concepts in a clear, practical way.
• Support adoption of AI‑enabled tools by building stakeholder trust in model outputs through transparency, explainability, documentation, and strong change management.
• Contribute to cross‑functional AI/ML governance forums and ensure alignment with global AI/ML initiatives, responsible AI principles, and CSL technology standards.
• Help establish repeatable playbooks, templates, and ways of working for AI use case development, deployment, measurement, and scale.

• Support evaluation, selection, and management of external data science, AI, and technology partners, including vendors supporting advanced analytics platforms, model development, deployment, and AI enablement.

• Translate business needs into vendor scopes of work, technical requirements, deliverables, timelines, and success measures.

• Review vendor outputs to ensure they are fit for purpose, validated, well documented, and aligned with CSL standards for data quality, governance, privacy, compliance, and technical execution.

• Partner with vendors and internal teams to resolve risks, dependencies, and delivery issues across the AI/DS project lifecycle.

• Support evaluation, selection, and management of external data science, AI, and technology partners, including vendors supporting advanced analytics platforms, model development, deployment, and AI enablement.

• Translate business needs into vendor scopes of work, technical requirements, deliverables, timelines, and success measures.

• Review vendor outputs to ensure they are fit for purpose, validated, well documented, and aligned with CSL standards for data quality, governance, privacy, compliance, and technical execution.

• Partner with vendors and internal teams to resolve risks, dependencies, and delivery issues across the AI/DS project lifecycle.

• Stay current with emerging AI/ML techniques, enterprise AI platforms, life sciences analytics trends, and evolving responsible AI and regulatory considerations.

• Identif…