Lead, Decision Data & Intelligence Enablement

Band

Level 5

The Lead, Decision Data & Intelligence Enablement plays a critical role in enabling enterprise decision‑making at Novartis by ensuring that data is decision grade and intelligence is applied where it creates impact.

• Own and establish decision‑grade data products that support enterprise portfolio decisions, ensuring that data is decision‑grade and AI & automation capabilities are proactively identified, shaped, and scaled to enable enterprise decision‑making impact.
• Lead the identification, prioritization, and scaling of high‑impact AI, automation, and advanced analytics use cases, ensuring trusted data foundations, clear confidence signals, and guardrails for adoption.

The incumbent works across Andromeda & Governance Enablement (AGE), Enterprise Portfolio Insights (EPI), Data, Digital & IT and Research‑Development‑Commercial (RDC) teams to ensure that data trust is visible, measurable, and embedded into system design and execution at scale.

The role actively drives the AI and automation agenda within AGE, ensuring that opportunities are identified early, barriers are removed, and adoption is scaled with measurable enterprise impact.

• Own and define a portfolio of decision‑grade data products for enterprise portfolio decision making, including standards, stewardship, and accountability models across systems and partners.
• Establish and maintain data quality standards and trust indicators, making data fitness for decision use visible, measurable, and transparent at leadership level.
• Drive enterprise trust reporting, proactively identifying recurring trust issues and prioritizing systemic prevention over repeated remediation.
• Identify, prioritize, and lead high‑impact AI, automation, and advanced analytics use cases, ensuring they are grounded in trusted data and scaled with clear business impact.
• Act as a key driver of the AI & automation agenda, translating opportunities into scalable, enterprise‑grade capabilities.
• Act as the primary accountability point for decision trust issues, ensuring clear communication of risks, confidence levels, and systemic resolution.

• Adoption of a clearly defined portfolio of decision‑grade data products and standards across AGE.
• Measurable improvement in data trust indicators for priority data products over time.
• Reduction in recurrence of data trust issues through proactive monitoring and design‑time prevention.
• Delivery and scaled adoption of high‑impact AI and automation use cases, including agent‑based capabilities, enabling trusted and self‑service access to decision data.
• Measurable improvement in decision‑making efficiency and speed enabled by AI and automation (e.g., reduced manual effort, faster access to insights).

• 7+ years of relevant pharmaceutical/AI/Consultant industry experience.
• Bachelor’s degree required; advanced degree preferred.
• Experience defining and managing enterprise data products for decision‑making, including data quality standards, trust indicators, and governance models.
• Proven ability to establish and drive data stewardship and accountability across complex, multi‑system environments.
• Demonstrated track record of owning and driving AI, automation, or advanced analytics initiatives from identification and prioritization through to scalable adoption.
• Ability to translate AI and analytics into clear enterprise decision impact, including defining confidence signals, usage guidance, and guardrails.
• Strong ability to influence without authority and align stakeholders across business, digital, architecture, and delivery teams.
• Proven stakeholder negotiation skills, effectively managing trade‑offs (e.g., speed vs. quality, local vs. enterprise needs).
• Strong proactive problem‑solving, anticipating risks and roadblocks and driving mitigation actions across teams.
• Excellent executive communication, clearly articulating data trust, risks, confidence, and decision implications.

• Strong data and analytical literacy (e.g., SQL, Python, BI tools) and understanding of enterprise data architecture concepts.
• Experience in enterprise…