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Ai In information

What is an AI Engineer?

An AI Engineer is a professional who designs, develops, and implements artificial intelligence systems and applications. They use machine learning algorithms, deep learning frameworks, and data analysis techniques to create intelligent solutions that can perform tasks such as image recognition, natural language processing, and decision-making. AI Engineers often collaborate with data scientists and software developers to build models, train AI systems, and deploy them in real-world environments. Their work is crucial in advancing automation and making technology smarter across various industries.

What is the difference between Ai In vs Data Analyst?

AspectAi InData Analyst
Required CredentialsTypically a degree in AI, computer science, or related field; certifications in AI or machine learningDegree in statistics, mathematics, or related field; certifications in data analysis or visualization
Work EnvironmentTech companies, AI research labs, startups; focus on developing AI modelsBusiness, finance, healthcare sectors; analyze data to inform decisions
Employer & Industry UsagePrimarily in tech and AI-focused industriesAcross various industries including finance, healthcare, marketing

While both roles involve working with data, Ai In focuses on developing and implementing AI models, whereas Data Analysts interpret data to support business decisions. Ai In roles require specialized knowledge in AI and machine learning, while Data Analysts focus on data visualization and statistical analysis.

What job makes $10,000 a month without a degree?

In the field of AI, roles such as AI consultant, machine learning engineer, or data scientist can potentially earn $10,000 or more per month, especially with specialized skills, experience, and certifications. These positions often require strong programming knowledge, understanding of AI tools, and sometimes a background in mathematics or statistics, but they do not always require a formal degree if demonstrated expertise is available.

What are the key skills and qualifications needed to thrive as an AI Engineer, and why are they important?

To thrive as an AI Engineer, you need strong programming skills (especially in Python), a solid foundation in mathematics and statistics, and typically a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks like TensorFlow or PyTorch, experience with data modeling, and sometimes certifications in AI or data science are highly valued. Critical thinking, creativity, and effective communication help AI Engineers collaborate on complex projects and translate technical concepts for diverse audiences. These skills and qualities are crucial for developing innovative, reliable AI solutions that address real-world challenges.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership, strategic planning, and extensive experience, and they may be found in large tech companies or specialized AI firms with competitive compensation packages. Such salaries reflect the high demand and specialized expertise in the AI industry.

Which 3 jobs will survive AI?

AI In roles such as data analysts, AI specialists, and cybersecurity professionals are likely to persist because they require complex problem-solving, creativity, and human oversight. These jobs involve tasks that are difficult to fully automate, especially those requiring critical thinking, ethical judgment, and interpersonal skills.

How is AI being used in jobs?

AI is used in jobs to automate repetitive tasks, analyze large data sets, and improve decision-making processes. Roles such as AI specialists, data analysts, and machine learning engineers often require knowledge of programming languages like Python and familiarity with AI tools and frameworks. This technology enhances productivity and enables new capabilities across various industries.

What are some common challenges AI Engineers face when integrating artificial intelligence solutions into existing business systems?

AI Engineers often encounter challenges such as ensuring compatibility between new AI models and legacy systems, managing data quality and availability, and addressing scalability concerns. Effective communication with cross-functional teams, including data scientists, IT, and business stakeholders, is essential to understand requirements and mitigate potential roadblocks. Additionally, AI Engineers must balance innovation with ethical considerations and compliance, making it crucial to stay updated on industry standards and best practices.
What are popular job titles related to Ai In jobs in Michigan? For Ai In jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Ai In jobs in Michigan look for? The top searched job categories for Ai In jobs in Michigan are:
What cities in Michigan are hiring for Ai In jobs? Cities in Michigan with the most Ai In job openings:
Ai In jobs near you
Infographic showing various Ai In job openings in Michigan as of June 2026, with employment types broken down into 1% As Needed, 79% Full Time, 17% Part Time, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.

Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV

Dana Incorporated

Novi, MI • On-site

Full-time

Posted 26 days ago

Dana Incorporated rating

5.7

Company rating: 5.7 out of 10

Based on 75 frontline employees who took The Breakroom Quiz

389th of 417 rated machine equipment manufacturers

Job description

Job Purpose
The Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV is a senior leadership role responsible for defining and executing the company's enterprise-wide data strategy, with a strong focus on Master Data Management (MDM), data governance, and AI-driven transformation across Aftermarket (AFM) and Commercial domains.
This role leads the end-to-end data value chain-from master data integrity and governance to advanced analytics and AI-ensuring that enterprise data is trusted, unified, and actionable. The Sr. Director will partner closely with business, digital, and engineering leaders to embed data and AI into core commercial and operational processes, driving measurable outcomes in revenue growth, customer experience, and operational performance.
Job Duties and Responsibilities
Enterprise Data & AI Strategy Leadership
• Define and lead the enterprise data strategy, anchored in MDM, data governance, analytics, and AI, aligned to AFM and Commercial growth priorities.
• Establish a multi-year roadmap spanning master data, data platforms, analytics, and AI/GenAI capabilities.
• Act as a strategic advisor to executive leadership, shaping how data and AI drive competitive advantage, revenue, and operational excellence.
• Build and lead a high-performing global organization across MDM, data engineering, governance, analytics, and data science.
Master Data Management (MDM) & Data Governance
• Own and institutionalize enterprise MDM strategy and platforms across core domains (Customer, Product, Supplier, Pricing, Assets).
• Establish data ownership, stewardship models, and domain accountability across AFM and Commercial.
• Drive data standardization, harmonization, and lifecycle management to enable consistent reporting and AI readiness.
• Lead enterprise-wide data governance frameworks, including policies, quality management, lineage, and metadata.
• Ensure compliance with regulatory, privacy, cybersecurity, and intellectual property standards.
• Define and track data quality KPIs and drive continuous improvement across business domains.
Data Platforms & Architecture
• Own the strategy and evolution of modern data platforms, including lakehouse architectures, real-time data pipelines, and semantic data layers.
• Ensure platforms are AI-ready, scalable, secure, and optimized for cost and performance.
• Partner with Enterprise Architecture and Cybersecurity to enforce standards, data models, and integration patterns.
• Enable seamless integration of ERP, CRM, supply chain, and engineering data into unified data products.
Analytics, AI & Advanced Capabilities
• Define and scale a portfolio of high-impact analytics and AI use cases, including:
o Commercial performance, pricing, and margin optimization
o Aftermarket demand forecasting and parts optimization
o Customer insights and segmentation
o Predictive maintenance and service optimization
o AI-enabled anomaly detection and operational intelligence
o Generative AI for commercial insights, automation, and decision support
• Lead the end-to-end AI lifecycle (ideation to production) with strong MLOps and governance practices.
• Establish a product-based data & analytics operating model, delivering reusable, scalable data products and AI capabilities.
AFM & Commercial Business Alignment
• Partner with AFM and Commercial leaders to translate business strategy into data, MDM, and AI solutions.
• Ensure master and transactional data enable core commercial processes including quoting, pricing, forecasting, and customer engagement.
• Drive use of data and AI to enhance revenue growth, profitability, and customer experience.
• Act as the primary data and AI leader for AFM and Commercial transformation initiatives.
Education and Qualifications
Required
• Bachelor's degree in Computer Science, Engineering, Data, or related field (Master's preferred).
• 12-15+ years of experience in enterprise data, MDM, analytics, and AI leadership roles.
• Proven track record leading enterprise-scale MDM and data transformation programs.
• Deep expertise in data governance, master data domains, and modern data architectures.
• Experience delivering AI/analytics solutions with measurable commercial impact.
• Strong leadership experience managing global, cross-functional teams and transformation programs.
Preferred
• Hands-on experience with MDM tools/platforms, data quality frameworks, and metadata management.
• Familiarity with AI/ML, MLOps, and GenAI applications in commercial or industrial settings.
• Experience in manufacturing, aftermarket, or asset-intensive industries.
• Exposure to OT/IT convergence and engineering data ecosystems.
• Strong executive presence with ability to influence at C-suite level.
Measures of Success
• Business value delivered through data, MDM, and AI initiatives (revenue, margin, cost, productivity)
• Enterprise data quality, consistency, and governance maturity
• Adoption and impact of analytics and AI in AFM and Commercial operations
• Speed and scalability of data product and AI delivery
• Effectiveness of MDM in enabling enterprise-wide insights and processes
Join our team of 28,000 problem solvers who are fostering a culture of innovation by leveraging the diverse perspectives of our global team. We believe in facing challenges head-on by finding opportunity and uncovering possibility, where roadblocks and barriers become targets instead of obstacles. We are One Dana with limitless opportunity.
Our Values
  • Value Others
  • Inspire Innovation
  • Grow Responsibly
  • Win Together

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