Director of AI and Data Solutions

Title: Director of AI and Data Solutions

Agency: VP DIGITAL TRANSFORMATION & TECHNOLOGY

Location: Remote

FLSA: Exempt

Hiring Range:

Full Time or Part Time:

Additional Detail

Job Description:
As the Director of AI & Data Solutions at ODU, you will lead the strategic vision, development, and implementation of artificial intelligence, machine learning, data engineering, and data integration initiatives. You will play a key leadership role in transforming how data is used to inform decision-making, improve services, and enable intelligent automation across the university. This position requires strong cross-functional collaboration with IT, academic departments, institutional research, and external partners to deliver scalable and ethical data-driven solutions aligned with the university's goals.

Minimum Qualifications:

Master's Degree in Computer Science, AI, Data Science, Engineering, Information Systems, or a related field. Or a bachelor's in a related field with prior related work experience equivalent to a master's degree.

• Strong programming skills in Python, R, SQL, or Java and experience with modern AI/ML frameworks (e.g., TensorFlow, PyTorch).
• Deep understanding of data architecture, data modeling, pipelines, and warehousing in a complex IT environment.
• Exceptional leadership, communication, and stakeholder engagement skills.
• Experience with cloud platforms and services (e.g., AWS, Azure, Google Cloud), especially those used for AI and big data processing.
• Hands-on experience with data integration tools such as Informatica, Boomi, Mulesoft, or equivalent.
• Demonstrated experience in AI/ML solution development, data engineering, and enterprise-level data integration.
• Flexibility to work outside of standard office hours as needed to support system implementations and upgrades.
• Commitment to ongoing professional development in AI, machine learning, and data solutions.

Additional Considerations:

• A Ph.D. is preferred.
• Experience working in a higher education or research-intensive environment.
• Familiarity with enterprise data models and common higher education systems (e.g., SIS, LMS, CRM).
• Experience implementing data lakes, lakehouses, or mesh architectures.
• Certifications in AI/ML, cloud architecture, or data integration tools.