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About the Role

The DTNA Data Intelligence Hub builds AI and data analytics solutions to transform DTNA into the intelligent company of tomorrow. In this role, you will drive the development of innovative data and AI products using DTNA’s data foundation and emerging AI technology. You will deliver solutions leveraging our data and platforms, and explore new AI opportunities. This role is responsible for driving data technical architecture, AI solution implementation, and cross-region collaboration, ensuring enterprise platforms are AI-ready for rapid, responsible development and scaling of AI products.

Responsibilities

Provide architecture reviews and technical guidance for AI initiatives
Offer feasibility insights and scalability considerations during AI intake discussions
Identify high-impact AI and Data opportunities across business units and geographies
Coordinate with DTG data leads, architects, and other group-wide technical leaders to develop and execute comprehensive data and AI technology solutions
Collaborate with DTICI to create global data and AI architecture standards and solutions
Support localization of applicable global data and AI solutions to meet regional needs and regulatory requirements
Promote internal knowledge sharing through data and AI communities of practice
Ensure the DTNA enterprise data platform remains AI-ready and scalable
Direct collaboration with business leaders and directors to understand operational challenges, strategic priorities, and transformation goals
Ensure platforms and data are AI-ready — architecting data foundations so that data is discoverable, well-governed, and consumable by AI and machine learning systems
Partner directly with the business to identify high-value use cases and translate them into functional proof-of-concepts (POCs) and minimum viable products (MVPs) for AI products
Evaluate and pilot AI technology across the existing data stack and surrounding ecosystem
Champion an AI-first approach to data engineering and architecture
Operationalize and scale AI solutions — establishing the patterns, pipelines, and MLOps/LLMOps practices needed to move validated POCs/MVPs into reliable, monitored production
Embed responsible and governed AI practices — ensuring AI use aligns with data security, privacy, quality, and emerging AI governance standards throughout the lifecycle
Advise and upskill stakeholders — serving as a technical-yet-business-fluent advisor who helps functional teams and leadership understand what is possible with AI and how to prioritize it
Lead design and hands-on implementation of enterprise RAG and agent-based solutions
Lead architecture and hands-on implementation of enterprise AI Agents leveraging Retrieval-Augmented Generation (RAG), Semantic retrieval, Vector Search, tool orchestration and Mult Agent workflows
Define AI Quality metrics, evaluation frameworks, golden datasets, offline and online evaluation pipelines, LLM-as-judge workflows and regression testing to continuously improve AI Systems
Design Semantic and ontology-driven data models that enable enterprise AI , self-service analytics, intelligent reasoning and knowledge discovery
Evaluate and adopt emerging AI standards such as the Model Context Protocol (MCP) to enable secure, interoperable enterprise AI Agents and tool integration
Lead a team of AI and data professionals in driving innovation, operational efficiency, and strategic alignment across the enterprise
Conduct employee reviews, Impact talks, and other employee feedback and guidance
Lead employees in fostering their development plans
Recruit, mentor, and retain top AI talent
Foster a collaborative and inclusive team culture
Define team goals and performance objectives aligned with strategic priorities
Build high performing engineering teams through strategic hiring , technical interviewing , mentoring and talent development
Provide technical leadership through architecture reviews , design reviews , code reviews , AI Solution reviews and production readiness assessments

Requirements

Bachelor’s degree with focus in Technical or design field (Computer Science, Software Design, Data Science) from an accredited college/university and 7+ years of relevant experience, is required.
An attached resume is required
All other applicants must be legally authorized to work permanently in the country the position is located in at the time of application
Final candidate must successfully complete a criminal background check
Final candidate may be required to successfully complete a pre-employment drug screen
Contractors, professional services, or other contingent workers should confirm with their local agency if they are eligible to apply for FTE positions

Skills

Experience on major cloud analytics platforms
Experience with major big data analytics platforms
Experience with generative AI and major LLM models
Experience deploying and managing large cloud compute workloads for optimal speed and cost efficiency
Expert proficiency in SQL
Deep, hands-on expertise in data modeling (conceptual, logical, physical) and both relational and non-relational stores
Deep understanding of data engineering, data architecture
Proven experience designing and operating ETL/ELT pipelines and modern data platforms (e.g., data warehouse, data lake, or lakehouse architectures)
Demonstrated experience preparing and architecting data specifically for machine learning and AI consumption (feature readiness, quality, accessibility, governance)
Demonstrated ability to handle structured and unstructured data at scale — ingestion, cleansing, profiling, validation, and aggregation
Experience with streaming/real-time data architectures (e.g., Kafka, event-driven pipelines)
Experience with distributed computing frameworks
Experience with cloud-native AI services (Azure AI, AWS SageMaker)
Experience with containerization and orchestration
Hands-on work with agentic AI frameworks or orchestration tooling, and with major AI/data-platform ecosystems
Experience productionizing and scaling AI solutions from prototype to reliable, monitored production
Proven track record of delivering data and AI-driven business transformation
Strong business acumen and ability to communicate complex technical concepts to non-technical stakeholders
Experience working in multinational or matrixed organizations
Advanced degree in Computer Science, AI, Data Science, or a related field
Experience with large data sets and distributed computing a plus
GitHub or similar collaborative development hub a plus
Experience with Manufacturing, Finance, and Aftermarket data
Familiarity with regulatory frameworks and data privacy laws across regions
Multilingual capabilities or experience working across diverse cultural environments
Thought leadership through publications and speaking engagements

Location

Portland, OR US

Work Type

Hybrid (4 days per week in-office / 1 day remote)

Experience Level

7+ years of relevant experience
Advanced degree

Education Level

Bachelor’s degree with focus in Technical or design field (Computer Science, Software Design, Data Science)
Advanced degree in Computer Science, AI, Data Science, or a related field

About the Company

At Daimler Truck North America, we recognize our world is changing faster than ever before. By listening to the needs of today, we’re building to solve with cutting-edge solutions in sustainability and future driving technology across electric, hydrogen and autonomous.
These solutions, backed by years of innovative success and achievement, continue DTNA’s legacy as the undisputed industry leader.
Our evolving brand portfolio is second to none, including Freightliner Trucks, Western Star, Demand Detroit, Thomas Built Buses, Freightliner Custom Chassis, and Financial Services.
Together, we work as one team towards our envisioned future – building a cleaner, safer and more efficient tomorrow for all.
That is what we are working toward - for all who keep the world moving.

Equal Opportunity

EEO - Disabled/Veterans
Daimler Truck North America is committed to workforce inclusion and providing an environment where equal employment opportunities are available to all applicants and employees without regard to race, color, sex (including pregnancy), religion, national origin, age, marital status, family relationship, disability, sexual orientation, gender identity and expression (including transgender and transitioning status), genetic information, or veteran status.