Director, AI & Data Science

CSL

. Develop and lead the AI and Data Science strategy for BARDS, aligned with CSL Behring’s Commercial, Medical, Market Access, and enterprise priorities .

Posted 10/13/2026full-timeUnited StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and leading AI and Data Science strategies, with a strong focus on advanced analytics, machine learning, and responsible AI governance. Proven ability to translate business needs into actionable data science use cases and manage cross-functional teams in a regulated industry.

Highest-signal resume keywords

AI/ML Strategy DevelopmentMachine Learning Model DeploymentDataiku Platform ExpertiseCross-Functional Team LeadershipHealthcare Analytics Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceAdvanced AnalyticsMachine LearningGenAIPredictive ModelingStatistical AnalysisCare Gap AnalyticsForecastingPatient-Finding ModelsAI Governance

Soft Skills

Executive CommunicationInfluencing StakeholdersChange ManagementTeam LeadershipProblem-Solving

Tools & Technologies

AWSSnowflakeDatabricksDataikuPythonRScikit-learnTensorFlowPyTorchLLMs

Industry Keywords

Pharmaceutical IndustryHealthcare AnalyticsRegulatory ComplianceData GovernanceMarket AccessCommercial ModelsMedical AffairsRare DiseasePatient IdentificationAI Ethics

Tech Stack

Tools & technologies

AWSCloudPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Develop and lead the AI and Data Science strategy for BARDS, aligned with CSL Behring’s Commercial, Medical, Market Access, and enterprise priorities
Build and maintain an AI/data science roadmap prioritizing high-value use cases
Establish AI use-case intake and prioritization based on value, feasibility, data readiness, risk, effort, governance, and ROI
Lead development and deployment of advanced analytics, predictive modeling, machine learning, GenAI, and agentic AI solutions
Oversee AI/ML use cases from problem framing and feasibility through proof of concept, pilot, production, adoption, and value measurement
Lead patient-identification and rare-disease patient-finding models, including PID/SID and HAE use cases
Develop AI-enabled Medical Analytics capabilities, including care-gap identification, medical-impact measurement, HCP insights, evidence-generation support, field-medical effectiveness, and disease-education opportunities
Partner with Tender and Pricing teams on tender performance, price erosion, market dynamics, competitive behavior, and decision support
Enable forecasting and market-insight solutions for analog identification, scenario planning, signal detection, and insight generation
Establish responsible-AI governance standards covering validation, explainability, human review, bias, factuality, faithfulness, auditability, data quality, privacy, and risk
Define controls, documentation, monitoring, user training, and business ownership for AI-enabled tools
Lead strategic use and adoption of Dataiku and related AI/ML platforms
Define the Dataiku operating model, including intake, prioritization, governance, enablement, value tracking, and deployment
Build internal AI and data-science capability through training, coaching, best-practice sharing, reusable frameworks, and hands-on enablement
Lead cross-functional teams across Commercial, Medical Affairs, Market Access, I&T, Data Governance, and external partners
Drive alignment, adoption, accountability, change management, and implementation of high-priority AI/ML initiatives
Lead the AI/data-science portfolio, including prioritization, resourcing, governance, delivery milestones, and value tracking
Manage external vendors and partners; evaluate build-versus-buy opportunities and recommend platforms and partnerships
Define KPIs and ROI frameworks and communicate measurable business value to senior leadership and executives

Requirements

What you’ll need

Bachelor’s degree required in Data Science, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, Bioinformatics, Engineering, Epidemiology, Health Informatics, or a related quantitative discipline
Master’s degree or Ph.D. strongly preferred
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
Experience with rare disease patient identification, patient-finding models, HCP segmentation, care gap analytics, forecasting, Medical Analytics, Tender Analytics, or customer insights strongly preferred
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
Experience with Dataiku or similar enterprise AI/ML platforms strongly preferred
Familiarity with 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
Preferred capabilities include AI/data science capability scaling, AI governance, team or indirect-resource leadership, GenAI solutions, rare-disease diagnosis using real-world data, ROI/adoption frameworks, and publication, patent, conference, or thought-leadership experience

Benefits

Comp & perks

Employer-provided benefits; details available via CSL’s “How CSL Supports Your Well-being” benefits information
Inclusion and Belonging culture
Reasonable accommodation available for applicants with disabilities