Segment Data Analytics Leader

Job Overview

We are seeking an experienced and strategic Data and Analytics leader to drive segment-wide business engagement to drive data quality, advanced analytics solution that helps drive business transformation.  Reporting solid line to the Chief Data Officer, with a heavy dotted line into IS CIO, this role will be responsible for shaping the demand for data science, analytics, and AI solutions across business units, accelerating adoption of the enterprise analytics standards, and reducing legacy data systems and duplication. 

The ideal candidate will have a strong track record in building data-driven cultures, influencing business leaders, and implementing AI and analytics at scale. This is a highly cross-functional leadership role that will collaborate closely with business unit leaders to embed data and AI literacy and ensure that data is leveraged as a strategic asset for decision-making and innovation.

Key Responsibilities

1️. Business Engagement & Demand Shaping for Data, Analytics, and AI

• Partner with  Business Unit leaders to identify high-value analytics, data science, and AI opportunities.
• Shape and prioritize data-driven use cases aligned with business goals and enterprise solutions
• Establish a demand management framework to connect BU needs with data capabilities. Deliver $$ value targets.

2️. One Data Platform Adoption

• Drive sector-wide adoption of the One Data Platform by aligning with business requirements and scalable data products,  and delivering value-driven solutions.
• Act as a strategic advisor on data integration, self-service analytics, and advanced AI/ML capabilities.
• Partner with data technology team to enhance data accessibility, governance, and security.

3️. Legacy Data Systems Reduction & Modernization

• Collaborate with business units to retire legacy data platforms and data copies,  and transition to modern, scalable solutions.
• Define and track KPIs for legacy system decommissioning and ensure smooth data migrations.
• Partner with IT, data engineering, and business teams to streamline data architecture and optimize infrastructure costs.

Other Responsibilities

4️. Data & AI Literacy & Culture Building

• Lead Segment-wide Data & AI Literacy initiatives to ensure business leaders and teams are confident in using data for decision-making.
• Execute a literacy  training strategy to enable self-service analytics, AI adoption, and data-driven decision-making at all levels.
• Foster a data culture by driving awareness, best practices, and success stories across the segment

5️. Talent Development, Leadership & Cross-Functional Collaboration

• Serve as a trusted data & analytics leader for the BU Spoke team and partner to the BU CIOs and their and business leaders.
• Develop talent with the Segment Analytics team to become more integrated a broader analytics leaders and influencers within their BUs
• Partner with HUB / Enterprise Data Governance, Engineering, and Analytics teams to align on standards, architecture and priorities.
• Drive a business-first mindset within the segment organization, ensuring that data and AI initiatives deliver tangible business outcomes for BUs.

What your background should look like:

Required Qualifications

• Experience: 10+ years of experience in data and analytics leadership roles, with a strong track record of business engagement and AI adoption.
• Business Acumen: Ability to translate business needs into data-driven strategies and deliver measurable impact.
• Technology Expertise: Deep understanding of enterprise data platforms, AI/ML, self-service analytics, and cloud data architectures.
• Leadership & Influence: Strong ability to engage, communicate, and influence senior executives and cross-functional teams.
• Change Management: Proven experience in driving organizational change, legacy system modernization, and data literacy initiatives.
• Data Strategy & Governance: Experience in data governance, data democratization, and regulatory compliance.

Preferred Qualifications

• Experience in manufacturing, healthcare, finance, or other data-rich industries.
• Knowledge of modern data architectures (e.g., Databricks, Snowflake, Azure, AWS, GCP).
• Strong understanding of AI ethics, data privacy, and regulatory frameworks.

Competencies