Director, Portfolio and Technology Enablement

Director, Portfolio and Technology Enablement

Introduction to role:

Are you ready to align analytics and AI to measurable outcomes that accelerate evidence creation and decision-making for patients and the business? As Director, Portfolio and Technology Enablement, you will shape a clear, value-led roadmap that reduces cycle time, improves quality and compliance, and turns data into trusted insight at scale. How would you orchestrate data products and intelligent automation to cut manual effort and raise confidence in operational decisions?

This role connects strategic vision with hands-on delivery. You will own the data product portfolio end-to-end, set OKRs and value metrics that guide investment, and lead a high-performing team to build governed, AI-ready capabilities. Partnering across functions and with external experts, you will speed adoption of modern platforms, models, and automation that help clinicians and colleagues act on evidence faster and more effectively.

Accountabilities:

• Data Strategy and Value Measurement: Define and lead the analytics roadmap tightly aligned with business outcomes; set OKRs and value metrics for data products, models, and automation; prioritize use cases that deliver measurable impact on portfolio execution, cycle time, quality, and compliance.
• Product Ownership and Adoption: Oversee all data products and their life cycles (including One Cockpit dashboards, OMED datasets, portfolio and operations reporting, and evidence delivery telemetry); set product vision, manage backlog and releases, drive user adoption, and define service-level objectives for data freshness, reliability, and usability.
• Governance and Stewardship: Establish and enforce data quality, lineage, and access controls; implement stewardship, metadata management, and compliance practices (including GxP where applicable, privacy, and security); champion model governance and monitoring standards in partnership with Enterprise AI Governance to maintain trusted, audit-ready assets.
• Architecture and Platforms: Manage scalable, secure, and cost-effective platform architectures that support advanced analytics and AI (e.g., Premium BI, Databricks, Power Platform); partner with Enterprise AI Architecture and Data Platform teams to design an AI-ready warehouse and optimize pipelines, feature stores, and inference pathways.
• Analytics and Modeling: Deliver portfolio insights and validated models (forecasting, resource optimization, risk prediction, throughput and cycle-time analytics) that inform operational decision-making and drive value.
• Intelligent Automation: Eliminate manual processes using AI and RPA; scale automation with Power Automate, Copilot Studio, and App Store Power Apps to improve speed, accuracy, and compliance; establish automation pipelines and playbooks for repetitive evidence delivery workflows.
• Innovation Oversight: Guide responsible adoption and integration of emerging tools and platforms (e.g., RAGify, Databricks, Copilot Studio, Trial View, AZ Brain, Copilot in Cockpit); evaluate pilots, codify patterns, transition successful proofs-of-concept into supported products, and embed lessons learned into reusable playbooks and training.
• Stakeholder Engagement and Enablement: Collaborate with internal leaders and external partners (including CROs and relevant vendors such as Evinova) to co-create high-impact product solutions and AI use cases; accelerate adoption through targeted mentoring, office hours, and enablement materials.
• Team Leadership and Capability Building: Lead and mentor a cross-functional analytics and data engineering team; foster an AI-first culture focused on experimentation, learning, and delivery excellence; establish structured learning programs and create stretch assignments to build capability in automation/AI, product management, and data stewardship.
• Knowledge Sharing and Mentoring: Act as mentor and coach across adjacent functions; lead communities of practice and learning sessions; publish standards, patterns, and playbooks that raise capability across the broader evidence community.
• Risk Management and Compliance: Proactively manage data accuracy, privacy, security, and model risk; implement controls for sensitive data, bias, drift, and explainability; oversee operational and technology risks with documented mitigations and continuous monitoring.
• Enterprise Representation: Represent the organization in enterprise technological advancement initiatives; ensure alignment with standards and strategies; influence enterprise roadmaps and contribute patterns and guidelines for AI-ready evidence systems; disseminate through communities and enablement sessions.
• Resource Allocation and Reporting: Optimize team and vendor resources to deliver the roadmap; provide transparent updates on progress, value realized, risks, and opportunities; maintain dashboards for portfolio analytics, automation impact, and model performance; support internal audits and regulatory inspections related to data, analytics, and AI.

Essential Skills/Experience:

• Proven ability to lead an analytics roadmap aligned with business outcomes, defining OKRs and value metrics for data products, models, and automation, and prioritizing high-impact use cases that improve portfolio execution, cycle-time, quality, and compliance.
• Demonstrated product ownership across the full life cycle for data products (e.g., One Cockpit dashboards, OMED datasets, portfolio and operations reporting, evidence delivery telemetry), including product vision, backlog, release planning, user adoption, and service-level objectives for freshness, reliability, and usability.
• Expertise in data governance: data quality, lineage, and access controls; stewardship and metadata management; compliance frameworks including GxP where applicable, privacy, and security; experience championing model governance and monitoring with Enterprise AI Governance.
• Experience managing scalable, secure, and cost-effective architectures supporting advanced analytics and AI (Premium BI, Databricks, Power Platform), partnering to design AI-ready warehouses and optimizing pipelines, feature stores, and inference pathways.
• Track record delivering portfolio insights and validated models (forecasting, resource optimization, risk prediction, throughput and cycle-time analytics) that inform operational decisions.
• Experience spearheading intelligent automation using AI and RPA, scaling solutions with Power Automate, Copilot Studio, and App Store Power Apps, and establishing automation pipelines and playbooks.
• Capability to guide responsible adoption and integration of emerging tools and platforms (RAGify, Databricks, Copilot Studio, Trial View, AZ Brain, Copilot in Cockpit), including pilot evaluation, pattern codification, and productization.
• Strong stakeholder engagement skills, collaborating with internal leaders and external partners (including CROs and platform/tool vendors such as Evinova) to co-create product solutions and AI use cases and accelerate adoption via mentoring and enablement.
• Leadership experience building and mentoring high-performing analytics and data engineering teams; fostering an AI-first culture; establishing structured learning and stretch assignments in automation/AI, product management, and data stewardship.
• Demonstrated impact in knowledge sharing: leading communities of practice, learning forums, and publishing standards, patterns, and playbooks to raise capability across evidence teams.
• Proficiency in risk management for data and models, including controls for sensitive data, bias, drift, and explainability; managing operational and technology risks with documented mitigations and continuous monitoring.
• Experience representing functions in enterprise technology initiatives, aligning with standards and strategies, influencing roadmaps, and contributing patterns and guidelines for AI-ready evidence systems and processes.
• Strength in resource allocation and transparent reporting; maintaining dashboards for analytics, automation impact, and model performance; supporting audits and regulatory inspections related to data, analytics, and AI.

Desirable Skills/Experience:

• Hands-on design of feature stores, inference pathways, and performance-optimized pipelines in Databricks and related platforms.
• Practical experience applying model governance frameworks, monitoring telemetry, and explainability at scale in regulated environments.
• Depth in GxP validation approaches for data and analytics systems, audit readiness, evidence preparation, and remediation.
• Experience building reusable automation playbooks, enablement materials, and training that accelerate adoption across diverse stakeholder groups.
• Familiarity with vendor ecosystems (e.g., Evinova) and CRO collaboration patterns for evidence delivery and AI-enabled workflows.
• Background translating complex operational and scientific requirements into measurable product value, with dashboards that connect insights to action.

Why AstraZeneca:

Join a mission-driven, research-led enterprise where evidence and data shape real clinical decisions. You will collaborate with strategic partners across science and commercial functions, work with cutting-edge platforms, and help transform how physicians use evidence to improve outcomes—especially in cancer. We bring unexpected teams into the same room to spark bold ideas, value kindness alongside ambition, and share knowledge freely so you can experiment, learn, and scale impact. Your leadership will translate analytics and AI into tangible advances that move our pipeline forward and make a difference for patients.

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Annual base salary for this position ranges from 0.00 to 0.00.

AstraZeneca is committed to providing fair and equitable compensation opportunities to all colleagues. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The range provided in this posting represents an offer pay range used in a majority of situations. The base pay offered will vary depending on multiple individualized factors, including the candidate's skills and experience, job-related knowledge, and other specific business and organizational needs.  In some cases, offers outside the range may also be considered to address unique circumstances.

In addition, our permanent positions offer an annual Variable Pay Bonus/Short Term Incentive opportunity as well as eligibility to participate in our equity-based long-term incentive program (if applicable to role).  Benefits offered for permanent roles include a competitive Flex Benefits & Retirement Savings Program, 4 weeks’ paid vacation, and annual Personal Days. Fixed Term Contract/Temporary positions (excluding students) are offered a Contract Benefits Program.

We are using AI as part of the recruitment process.

This advertisement relates to a current vacancy.

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.