Senior Manager, Data Science, AI and Advance Analytics for 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. Cross‑Functional Collaboration 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. Vendor & Partner Management 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. Innovation & Continuous Improvement Stay current with emerging AI/ML techniques, enterprise AI platforms, life sciences analytics trends, and evolving responsible AI and regulatory considerations. Identify opportunities to apply advanced analytics and AI to CSL’s Commercial and Medical priorities, including use cases involving RWD/RWE, claims, EHR, specialty pharmacy, HCP/HCO, patient journey, and customer engagement data. Contribute to establishing KPIs, measurement frameworks, and value realization approaches to assess business impact, adoption, and ROI of data science and AI initiatives. Support continuous improvement of CSL’s AI operating model, including governance, delivery methods, technical standards, and stakeholder engagement. Contribute to internal thought leadership, capability building, and external innovation opportunities where appropriate, including publications, conference abstracts, or IP opportunities. Qualifications Education Bachelor’s degree in data science, AI, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline (required). Master’s degree in a relevant field is strongly preferred. Experience 5 to 7 years of experience in advanced analytics, data science, or (Gen)AI within pharma, biotech, or healthcare settings. Demonstrated track record of building and deploying machine learning models in commercial or clinical contexts. Experience contributing to end-to-end AI/ML initiatives, from ideation and scoping through to deployment and monitoring. Familiarity with commercial and medical pharma analytics, such as patient identification, HCP segmentation, forecasting, or competitive intelligence. Experience with rare disease patient‑finding models is a plus. #J-18808-Ljbffr CSL