Enterprise Analytics AI Manager

Daimler Truck North America•Charlotte, NC

14h•Hybrid

About The Position

Lead the evolution of business intelligence into AI-enabled analytics, using the enterprise AI platform to create trusted analytics, intelligent dashboards and decision-support capabilities while sustaining strong data management, governance, quality and partnership with IT and AI Engineering. About Daimler Truck Financial Services Daimler Truck Financial Services is the financial arm supporting the mission of Daimler Truck’s global transportation business. We deliver innovative financing, leasing, fleet-management and digital services to help our customers operate efficiently, sustainably and competitively. As DTFS North America expands the use of artificial intelligence across business processes and decision-making, we are strengthening the capabilities that turn trusted data into actionable insights.

Requirements

BS/BA and at least 8 years of progressive experience in business intelligence, analytics, data management, data products, enterprise technology or a related field, including people or program leadership experience.
Experience leading enterprise analytics or data capabilities, including data warehouses, operational data stores, data modelling, data integration, SQL and analytical reporting.
Demonstrated ability to translate business strategy and executive information needs into product roadmaps, analytics solutions and measurable outcomes.
Experience partnering across business, data governance and IT teams to deliver secure, scalable and supportable enterprise solutions.
Strong knowledge of data governance, data quality, metadata, lineage, access management, privacy and compliance practices.
Working knowledge of AI-enabled analytics approaches, including natural-language interaction with data, automated insight generation, intelligent dashboards and predictive analytics.
Excellent communication, consulting and stakeholder-management skills, including the ability to explain analytical and technical concepts to non-technical audiences.
Proven ability to lead, coach and develop teams in a matrixed environment.
Bachelor’s degree in Computer Science, Information Systems, Data Science, Mathematics, Statistics, Finance, Business Administration or a related field.

Nice To Haves

Experience modernizing a traditional BI or reporting organization toward AI-enabled analytics and product-oriented delivery.
Experience with enterprise AI, cloud data or modern analytics platforms and their integration with governed enterprise data.
Experience in financial services, commercial vehicle finance, leasing, credit, risk, operations, sales or fleet services.
Knowledge of statistical, mathematical, predictive and prescriptive modelling techniques.
Experience with agile delivery, human-centered design, product management and experimentation methods.
Experience supporting organizational transformation, enterprise portfolio initiatives or large-scale change programs.
Advanced degree in a relevant discipline.
A strategic mindset with strong execution discipline, able to set a future-state vision while delivering practical near-term value.
A product and customer orientation that starts with business decisions and user needs, not with tools or reports.
A governance-first mindset that treats data quality, privacy, security, documentation and trust as essential product requirements.
Curiosity about emerging AI analytics capabilities and sound judgment about where they create meaningful business value.
Strong business acumen and the ability to connect analytics investments to growth, efficiency, risk management, customer experience and decision quality.
Inclusive, accountable leadership that builds capability, encourages collaboration and develops talent.

Responsibilities

Define and own the DTFS North America AI analytics vision, strategy and roadmap, aligned with business priorities and the broader Data and AI strategy.
Lead the shift from legacy reporting and visualization approaches to AI-platform-enabled analytics, intelligent dashboards, conversational insight experiences and decision-support capabilities.
Partner with executive and functional leaders to translate business questions into a prioritized portfolio of analytics products, use cases and measurable outcomes.
Work in close partnership with the IT DSS team on architecture, data integration, platform enablement, security, application dependencies, release planning, support and operational sustainability.
Provide business and technology leadership for the Data Management Domain, including operational data stores, data warehouses, semantic and analytical data models, data pipelines and enterprise reporting foundations.
Ensure analytics products use governed, accurate, timely and well-documented data, with clear ownership, lineage, definitions, quality controls and appropriate access.
Lead and mature data governance practices in partnership with Data Stewards, including standards, metadata, documentation, privacy, retention, access and compliance requirements.
Build and maintain an AI analytics product roadmap and backlog; prioritize work based on business value, risk, urgency, feasibility and strategic alignment.
Enable diagnostic, predictive and prescriptive analytics by combining strong business context, trusted data and appropriate AI and analytical methods.
Adapt analytics capabilities and data foundations to support transformation programs, portfolio initiatives and changes to business processes or operating models.
Establish product performance measures and adoption feedback loops for analytics and dashboard solutions, and use them to improve usability, trust and business value.
Drive an AI- and data-driven culture through stakeholder consulting, communication, training, self-service enablement and change-management activities.
Lead, coach and develop the AI Analytics team; allocate talent and capacity to maximize business outcomes while building expertise in AI-enabled analytics, data preparation, dashboard design and data management.
Manage relevant vendor and partner relationships and ensure solutions remain cost-effective, supportable and aligned with enterprise standards.
Represent AI analytics and data-management priorities in leadership, governance and cross-functional forums.