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.
QualificationsEducation- 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.
