Data Science and Business Intelligence Manager

Daimler Truck North America•Detroit, MI

7d•Hybrid

About The Position

The Manager of Data Science & Business Intelligence leads a cross-functional team of data scientists, data engineers, data analysts, business analysts, and developers delivering scalable data products, analytics, custom applications, automations, and cross-functional technology solutions for Detroit. The role sets direction for data strategy, digitalization, and custom solution delivery; translates business priorities into a practical roadmap and portfolio; and partners across IT and business functions to deliver secure, reliable, reusable, and sustainable solutions. It combines people leadership, strategic planning, technical judgment, project and product delivery, and operational accountability. The manager defines and executes a multi-year data, business intelligence, and custom-solutions strategy aligned with Detroit and enterprise objectives. The role oversees data architecture and integration, data governance, solution delivery and lifecycle management, and the team portfolio, vendors, budgets, capacity, dependencies, operational support, technical debt, and delivery risks. The manager works with business and global teams to improve decision-making, service delivery, technology standardization, and adoption of reusable data and technology solutions. This position is based in Detroit, Michigan, and follows a hybrid schedule of four days per week in-office and one day remote. Relocation assistance is not available.

Requirements

Bachelor’s degree in computer science, data science, statistics, mathematics, engineering, information systems, business analytics, or a related field, or an equivalent combination of education and relevant experience.
At least 8 years of progressive experience in data science, business intelligence, advanced analytics, data engineering, or related information technology disciplines.
At least 2 years of formal people leadership or demonstrated experience leading and developing analytics or data teams.
Experience translating ambiguous business needs into prioritized roadmaps, requirements, analytical approaches, and scalable production solutions.
Strong working knowledge of data architecture, dimensional and semantic modeling, data warehouses or lakehouses, integration patterns, ETL/ELT, APIs, metadata, data quality, and governance.
Proficiency with SQL and experience with Python, R, or a comparable analytical programming language.
Experience with business intelligence and visualization platforms such as Microsoft Power BI or comparable tools, including dashboard design and governed self-service analytics.
Experience with cloud-based data and analytics platforms such as Microsoft Azure, Amazon Web Services, Google Cloud, Databricks, or Snowflake.
Understanding of statistical analysis, forecasting, experimentation, machine learning, model evaluation, and controls for responsible deployment and monitoring of analytical and AI solutions.
Demonstrated ability to lead cross-functional initiatives, influence without direct authority, manage competing priorities, and communicate effectively with technical and non-technical stakeholders.
Experience managing projects, products, vendors, budgets, and operational support in a complex enterprise environment.

Nice To Haves

Master’s degree in a quantitative, technical, or business discipline.
Experience applying analytics in commercial-vehicle, automotive, manufacturing, supply chain, quality, connected-product, service, or research and development environments.
Hands-on experience with Microsoft Fabric, Azure Data Factory, Databricks, Snowflake, GitHub, CI/CD, or related modern data-stack capabilities.
Experience establishing data products, semantic models, master data, data catalogs, data lineage, data observability practices, and data and application governance.
Experience with MLOps, model monitoring, responsible AI, generative AI, or natural-language solutions in an enterprise setting.
Knowledge of manufacturing data environments, including manufacturing execution systems, industrial data, telemetry, or product-development data.
Relevant certifications in cloud, data engineering, analytics, project management, agile delivery, or data governance.
Experience leading organizational change, building data literacy, and increasing adoption of standardized products and services.

Responsibilities

Provide forward-thinking leadership to a global team of data and business intelligence professionals, developers, and cross-functional technology personnel; establish a vision, priorities, delivery standards, and development plans.
Recruit, train, coach, mentor, and manage the data analytics and custom solutions development team; assign work, provide guidance, evaluate performance, and build an inclusive, accountable, high-performing environment.
Define and execute a multi-year data, business intelligence, and custom-solutions strategy and roadmap aligned with Detroit and enterprise objectives.
Assist in defining and maintaining data architecture strategy and oversee data integration efforts that consolidate and transform information into governed repositories and reusable data products.
Improve data and information flow, reliability, scalability, performance, accessibility, and storage while reducing redundancy and supporting appropriate access within security, privacy, and compliance boundaries.
Partner with manufacturing, research and development, service, quality, finance, and other functions to translate requirements into analytics products, dashboards, custom applications, integrations, automation, and decision-support solutions.
Oversee the solution lifecycle from discovery and requirements gathering through architecture, design, development, validation, deployment, adoption, support, maintenance, and retirement.
Ensure work is delivered on time, within budget, and in accordance with project portfolio and execution standards.
Establish data governance for quality, definitions, lineage, access, security, privacy, documentation, model use, and lifecycle management in partnership with enterprise teams.
Define key performance indicators and measurement frameworks and ensure dashboards and reports deliver consistent, trusted, decision-ready information.
Identify and prioritize opportunities for predictive analytics, machine learning, optimization, and responsible generative AI.
Collaborate with global teams to define standards and processes supporting strategic growth, technology standardization, and consistent IT service operations and delivery.
Communicate analytical findings, business value, technical trade-offs, risks, and recommendations to technical teams, business partners, and senior leaders.
Advise on data, analytics, application, and cross-functional technology initiatives.
Manage the team portfolio, vendors, budgets, capacity, dependencies, operational support, technical debt, and delivery risks.
Balance incoming demand and strategic initiatives with the reliability of business-critical services.
Promote reusable solutions, self-service analytics, technical standards, continuous improvement, and knowledge sharing.
Use relevant industry, commercial, and technology insights to challenge the status quo while maintaining reliable day-to-day services.