Director, AI & Data Science

CSL•Waltham, MA

23h

About The Position

CSL is accelerating innovation to deliver greater impact for patients through data, analytics, digital transformation, and artificial intelligence. Within the Business Analytics, Reporting, Data & AI Strategy, BARDS, organization, the Director, AI & Data Science will play a critical leadership role in shaping and advancing CSL Behring’s Commercial and Medical AI agenda. This role will lead the strategy, development, governance, and deployment of advanced analytics, machine learning, GenAI, and AI-enabled decision support capabilities across CSL Behring’s Commercial, Medical, Market Access, and cross-functional business priorities. The Director will be responsible for translating complex business needs into scalable AI and data science solutions that improve decision-making, accelerate insight generation, support patient identification, optimize commercial execution, and enable measurable business and patient impact. The Director will serve as a senior thought partner to Commercial, Medical Affairs, Market Access, I&T, Data Governance, Legal, Compliance, Privacy, and external partners. This role will be accountable not only for developing and deploying AI/ML solutions, but also for building the operating model, governance framework, adoption strategy, and capability roadmap required to responsibly scale AI and data science across the organization. The ideal candidate brings deep technical expertise, strong life sciences domain knowledge, experience leading cross-functional AI/ML programs from concept to production, and the ability to influence senior stakeholders in a matrixed global environment. This individual must be able to bridge technical innovation with business strategy, ensuring that AI and data science capabilities are not only innovative, but practical, trusted, governed, and tied to measurable outcomes.

Requirements

Bachelor’s degree required in Data Science, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Bioinformatics, Engineering, Epidemiology, Health Informatics, or a related quantitative discipline.
12 or more years of experience in data science, advanced analytics, AI/ML, GenAI, commercial analytics, healthcare analytics, or life sciences analytics.
5 or more years of experience leading cross-functional AI/ML or advanced analytics initiatives in pharma, biotech, healthcare, or a similarly regulated industry.
Demonstrated experience developing and deploying machine learning models in commercial, medical, clinical, or healthcare contexts.
Strong experience translating business problems into data science use cases and moving solutions from concept to pilot to production.
Experience leading AI/ML or GenAI strategy, operating model development, governance frameworks, and adoption planning.
Deep understanding of the pharmaceutical industry, including commercial models, medical affairs, market access, regulatory considerations, privacy, compliance, and data governance.
Strong understanding of responsible AI principles, including model explainability, bias, fairness, factuality, human-in-the-loop review, data privacy, risk management, and auditability.
Experience with cloud environments and modern data science infrastructure, including AWS, Snowflake, Databricks, or equivalent platforms.
Familiarity with modern machine learning and AI frameworks such as Python, R, scikit learn, TensorFlow, PyTorch, LLMs, retrieval-augmented generation, multi-agent workflows, and GenAI evaluation frameworks.
Strong ability to influence senior stakeholders, lead through ambiguity, and drive alignment across matrixed global teams.
Demonstrated ability to manage vendors, external partners, and cross-functional delivery teams.
Strong executive communication skills, with the ability to translate complex technical topics into clear business implications and recommendations.

Nice To Haves

Master’s degree or Ph.D. strongly preferred.
Experience with rare disease patient identification, patient-finding models, HCP segmentation, care gap analytics, forecasting, Medical Analytics, Tender Analytics, or customer insights strongly preferred.
Experience with Dataiku or similar enterprise AI/ML platforms strongly preferred.
Experience building or scaling AI/data science capabilities within a Commercial, Medical, or Market Access organization.
Experience designing AI governance models and working with Legal, Compliance, Privacy, I&T, and Data Governance teams.
Experience leading teams or indirect resources across internal employees, contractors, vendors, and business stakeholders.
Experience with GenAI solutions for insight generation, competitive intelligence, forecasting, content analysis, decision support, or workflow automation.
Experience with patient identification and rare disease diagnosis use cases using claims, EMR, lab, specialty pharmacy, CRM, and other real-world data sources.
Experience developing ROI models, adoption metrics, and business impact frameworks for data science and AI programs.
Strong publication, patent, conference, or thought leadership experience in AI, machine learning, data science, healthcare analytics, or life sciences innovation preferred.

Responsibilities

Develop and lead the AI and Data Science strategy for BARDS, aligned to CSL Behring’s Commercial, Medical, Market Access, and broader enterprise priorities.
Build and maintain a clear AI/DS roadmap that prioritizes high-value use cases across patient identification, rare disease diagnosis, Medical Analytics, Tender Analytics, forecasting, customer insights, competitive intelligence, and AI-enabled decision support.
Partner with senior Commercial and Medical leaders to identify business challenges where AI, machine learning, GenAI, and advanced analytics can create measurable value.
Establish a structured AI use case intake and prioritization framework, including business value, feasibility, data readiness, risk, level of effort, governance needs, and ROI.
Translate emerging AI trends, technologies, and regulatory considerations into practical strategies for CSL’s Commercial and Medical environment.
Lead the development and deployment of advanced analytics, predictive modeling, machine learning, GenAI, and agentic AI solutions across priority business areas.
Oversee AI/ML use cases from problem framing and feasibility assessment through proof of concept, pilot, production deployment, adoption, and value measurement.
Provide technical and strategic leadership for patient identification and rare disease patient-finding models, including use cases in PID/SID, HAE, and other therapeutic areas where earlier diagnosis and intervention are critical.
Lead the development of AI-enabled capabilities for Medical Analytics, including care gap identification, medical impact measurement, KOL and HCP insights, evidence generation support, field medical effectiveness, and disease education opportunities.
Partner with Tender and Pricing teams to identify AI and advanced analytics opportunities related to tender performance, price erosion, market dynamics, competitive behavior, and decision support.
Support forecasting and market insights teams by enabling GenAI and machine learning solutions for analog identification, scenario planning, signal detection, and insight generation.
Partner with I&T, Data Governance, Legal, Compliance, Privacy, Medical, and Commercial stakeholders to ensure responsible and compliant development and deployment of AI solutions.
Establish governance standards for AI/ML and GenAI solutions, including model validation, explainability, human-in-the-loop review, bias assessment, factuality, faithfulness, auditability, data quality, privacy, and risk management.
Ensure AI-enabled tools are designed with appropriate controls, documentation, monitoring, user training, and business ownership.
Represent BARDS in relevant AI, data governance, and cross-functional forums to ensure Commercial and Medical AI use cases are aligned with CSL’s broader enterprise AI strategy.
Develop practical frameworks to balance innovation with appropriate guardrails in a regulated life sciences environment.
Lead the strategic use and adoption of Dataiku and related AI/ML platforms within BARDS and across Commercial and Medical analytics teams.
Define the operating model for Dataiku use, including project intake, use case prioritization, governance, user enablement, value tracking, and scalable deployment.
Partner with business users, data scientists, analysts, and I&T teams to improve adoption of AI-enabled workflows and self-service analytics capabilities.
Build internal AI and data science capability through training, coaching, best-practice sharing, reusable frameworks, and hands-on enablement.
Ensure that AI and advanced analytics tools are embedded into business processes in a way that is practical, sustainable, trusted, and measurable.
Serve as a strategic bridge between business needs and technical execution across Commercial, Medical Affairs, Market Access, I&T, Data Governance, and external partners.
Build strong partnerships with senior stakeholders to ensure AI and data science solutions are aligned with business priorities and embedded into decision-making workflows.
Lead cross-functional working teams to define requirements, align success measures, resolve dependencies, and drive implementation of high-priority AI/ML initiatives.
Influence without authority across a matrixed organization to drive alignment, adoption, and accountability.
Support change management and adoption planning so business users understand, trust, and act on AI-generated insights.
Lead a portfolio of AI and data science initiatives across BARDS, ensuring clear prioritization, resourcing, governance, delivery milestones, and value tracking.
Manage external vendors and partners supporting AI/ML, GenAI, advanced analytics, platform development, and data science delivery.
Evaluate build-versus-buy opportunities and provide recommendations on vendor selection, AI platforms, external partnerships, and emerging technologies.
Ensure vendor solutions are aligned with CSL’s data architecture, governance expectations, responsible AI principles, and business objectives.
Represent CSL Commercial Data Science in external forums, vendor discussions, and cross-functional governance meetings.
Define KPIs and ROI frameworks to measure the impact of AI and data science initiatives.
Ensure each prioritized AI/DS use case has clear objectives, success measures, business ownership, adoption plans, and measurable outcomes.
Track and communicate business value, including improved patient identification, increased speed to insight, enhanced decision-making, efficiency gains, reduced reliance on external consulting, improved field execution, and stronger Medical and Commercial impact.
Translate technical outcomes into clear business narratives for senior leadership and executive stakeholders.