Data Science Supervisor II

dtna; Portland (OR); On-site; USD 90,000 - 130,000; Full time

Job summary

DTNA is seeking the DIH Data-AI Tech Lead to drive data architecture, AI solution implementation, and cross-region collaboration. You will shape data foundations, ontology and metadata layers, and AI-ready platforms to enable rapid, responsible AI development at scale.

This AI-first role requires hands-on design and deployment of enterprise AI Agents, RAG, vector search, and MLOps. You will partner with business leaders to translate high-value use cases into POCs and MVPs while upholding

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 leaders to develop data and AI solutions.
• Collaborate with global teams to create data and AI architecture standards.
• Support localization of global AI solutions to meet regional needs.
• Promote internal knowledge sharing through data and AI communities of practice.
• Ensure enterprise data platforms remain AI-ready and scalable.
• Lead design and hands-on implementation of enterprise AI Agents and RAG-based solutions.
• Define AI quality metrics, evaluation pipelines, and governance practices.

Job description

Inside the Role

The DTNA Data Intelligence Hub builds AI and data analytics solutions that enable our customers to be faster, safer, and more profitable, transforming DTNA into the intelligent company of tomorrow. By leveraging data from all business functions, including massive amounts of vehicle and automation telematics data, we develop data and AI solutions to improve our products, streamline manufacturing, and perfect our customers' experience.

In this exciting and challenging role as the DIH Data-AI Tech Lead, you will drive the development of innovative new data and AI products using DTNA's and Daimler Truck's data foundation along with emerging AI technology. You are a highly motivated candidate who is willing to quickly learn and understand new technologies and tools. You will deliver solutions using the scale of our data and platforms to support your product needs, and look for new opportunities in the evolving world of AI to bring to our data.

This role is responsible for driving data technical architecture, AI solution implementation, and cross-region collaboration. In addition to the existing responsibilities in the current position, the role has evolved to include a significant AI component, including ensuring the enterprise's platforms, pipelines, and data assets are AI-ready - enabling the rapid, responsible development and scaling of AI products in partnership with the business. This is an AI-first role: the architect is expected to default to AI-enabled approaches wherever they add value, and to continuously evaluate emerging AI technology across the data stack and surrounding ecosystem.

We Take Care of Our Team

What You Drive at DTNA

Technical Leadership

• 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 - particularly within Aftermarket, Manufacturing and Finance domains - to better understand operational challenges, strategic priorities, and transformation goals
• Ensure platforms and data are AI-ready - architecting data foundations (including semantic/ontology layers, metadata, lineage, and access patterns) 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, moving from concept to working prototype quickly
• Evaluate and pilot AI technology across the existing data stack and surrounding ecosystem - assessing tools, platforms, models, and vendors for fit, maturity, cost, and risk, and making recommendations that shape the architecture roadmap
• Champion an AI-first approach to data engineering and architecture - applying AI to accelerate data preparation, quality, documentation, and automation, and defaulting to AI-enabled solutions wherever they deliver measurable value
• 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 kn