Associate Director, Data Science & AI

Associate Director, Data Science & AI

CSL's organization is accelerating innovation to deliver greater impact for patients. With a project-led structure and a focus on collaboration, we're building a future-ready team that thrives in dynamic biotech ecosystems. Joining CSL now means being part of an agile team committed to developing therapies that make a meaningful difference worldwide.

About The Role

We are seeking a visionary and strategic leader to spearhead the development and commercialization of (Gen) AI solutions tailored for the life sciences industry. The Insights and Analytics team is dedicated to enabling improved decision making at CSL by leveraging superior data to identify actionable insights that drive enhanced performance. We collaborate closely with the US & International business, bringing insights and challenging ideas to empower smarter, data-driven decision-making.

You will spearhead the application of advanced analytics, machine learning, and AI solutions to unlock new insights, accelerate decision-making, and enhance commercial outcomes across CSL Behring's portfolio. You will focus on embedding AI and predictive modeling into core commercial processes, including patient identification, Medical and Tender Analytics, forecasting, and customer insights. In this role, you will serve as a bridge between CSL's commercial business needs and technical innovation, ensuring scalable and responsible use of AI to create measurable business impact. The ideal candidate brings deep domain expertise, a strong innovation mindset, and a proven track record of translating AI capabilities into impactful business solutions.

Major Responsibilities

• Lead development and deployment of advanced analytics and AI models to address critical commercial challenges (patient identification in rare disease, HCP segmentation, Medical and Tender use cases)
• In close partnership with ABCIA Commercial, Medical Affairs, and Market Access teams to ensure solutions are aligned with strategic business needs
• Translate business problems into well-defined data science use cases and develop proof-of-concept pilots through to production
• Drive adoption of AI-enabled tools across the commercial organization, ensuring business users understand and trust the outputs
• Partner with I&T and Data Governance to ensure responsible AI practices, data quality, and scalable infrastructure
• Drive CSL innovation in the field, leading to high visibility publications in top AI conferences and patents around Generative AI, reasoning, multi-agent systems, etc
• Establish KPIs and frameworks to measure ROI and business impact of data science projects
• Stay current with emerging AI/Machine Learning (ML) techniques, tools, and regulatory developments, and translate those into CSL's context
• Develop strong external partnerships (vendors and external partners) to bring cutting-edge AI capabilities into CSL and represent Commercial Data Science in cross-functional governance forums, ensuring alignment with global AI/ML initiatives

Qualifications

• Bachelor's in data science, AI, Computer Science, Statistics, Applied Mathematics, or related quantitative discipline. Master's degree preferred
• 10+ years of experience in advanced analytics, data science, or (Gen)AI within pharma/biotech or healthcare; commercial analytics experience strongly preferred.
• Strong track record of developing and deploying machine learning models in commercial or clinical contexts. Strong analytical and problem-solving skills, with the ability to interpret complex data and make informed decisions
• A deep understanding of the pharmaceutical industry, including regulatory requirements, market dynamics, and emerging AI technologies, with the foresight to identify and map both current and future AI opportunities, is preferred.
• Experience leading cross-functional AI/ML initiatives from concept through deployment.
• Familiarity with cloud environments (AWS and Snowflake), data engineering workflows, and modern ML frameworks (TensorFlow, PyTorch, scikit-learn) and familiarity with no-code data science SaaS tools

Additional Qualifications – Preferred

• Experience in rare disease patient-finding models a plus.
• Experience building evaluation frameworks for LLMs (factuality, faithfulness, bias, toxicity) and human-in-the-loop review
• Knowledge of responsible AI frameworks, data governance, and compliance in pharma settings. Experience Leading design, training/fine-tuning, and evaluation of ML/LLM/Agentic systems (retrieval-augmented generation, tool-use, routing, multi-agent workflows).