Data Science Supervisor II

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.
Posting Information
We provide a scheduled posting end date to assist our candidates with their application planning. While this date reflects our latest plans, it is subject to change, and postings may be extended or removed earlier than expected.
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 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
undefinedSupervisoryLead a team of AI and data professionals in driving innovation, operational efficiency, and strategic alignment across the enterpriseConduct employee reviews, Impact talks, and other employee feedback and guidanceLead employees in fostering their development plansRecruit, mentor, and retain top AI talentFoster a collaborative and inclusive team cultureDefine team goals and performance objectives aligned with strategic prioritiesBuild high performing engineering teams through strategic hiring , technical interviewing , mentoring and talent developmentProvide technical leadership through architecture reviews , design reviews , code reviews , AI Solution reviews and production readiness assessments
Knowledge You Should Bring
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.
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 SQLDeep, 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, pro