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  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,…