In A Nutshell
Drive the adoption of AI across our campus by empowering teams to integrate AI capabilities into their work, whether it’s embedded within custom applications, reporting, or intelligent agents
Responsibilities
- Develops enterprise data models at several levels of detail. Develops and maintains enterprise dimensional data structures. Specifies processes for maintenance of dimensions and sets of dimensions.
- Technical Leadership & Enablement: Lead and mentor our AI development team in building robust, scalable solutions. Empower our existing development teams to integrate AI capabilities into their custom applications and systems. Direct the development of custom AI agents and applications that solve specific campus needs. Establish and lead MLOps (Machine Learning Operations) practices, focusing on the continuous monitoring, tuning, and management of models in production. This includes developing processes to monitor model output for hallucinations, ensure validity and truthfulness, and implement automated pipelines for model retraining based on performance degradation or data drift.
- Directs, initiates and designs data/information management studies. Prepares recommendations for data/information management and/or resource plans having critical, organization-wide and/or institution-wide impact.
- Strategy and Vision: Develop a comprehensive AI and data science roadmap, identifying high-impact opportunities across academic and administrative functions. Act as a visionary, engaging with campus leaders and teams to understand their challenges and help them envision what’s possible with AI and data. Stay current on the latest advancements in machine learning, data science, and generative AI, evaluating their potential to solve our unique challenges. Product Management & Execution: Serve as the primary product manager for AI and data science projects, leading the full lifecycle from ideation to release. Gather requirements, translate them into actionable user stories for the AI development team, and manage project backlogs. Coordinate product releases, manage stakeholder communication, and develop training materials for end-users.
- Regularly leads, directs, analyzes and prepares recommendations for data management information needs across process domains and academic disciplines. Performs analysis, up to and including the most complex and advanced.
- Data and AI Foundation: Collaborate with data engineering and architecture teams to establish data priorities, ensuring a trusted, high-quality data foundation to fuel our AI efforts. Advise on best practices for data governance, MLOps, and ethical AI development.
- Agile Leadership: Lead and mentor your team in an agile development process, overseeing daily stand-ups, sprint planning, and retrospectives. Manage project backlogs and ensure a steady cadence of product delivery, helping the team prioritize tasks and remove blockers.
- People Development and Team Leadership: Foster a culture of servant leadership within your team, prioritizing the growth and well-being of your direct reports. Empower your team to make decisions, take ownership of their work, and grow their skills. Act as a coach and mentor, helping your team members overcome obstacles and achieve their professional goals.
- Designs overall sourcing strategies. Designs and maintains communication across functional areas or disciplinary domains.
- Communication & Literacy: Act as a subject matter expert, communicating the value and application of both generative AI and machine learning to diverse, non-technical audiences. Help campus users understand how to use these technologies, sometimes blending both approaches into a single cohesive product.
Skillset
- Demonstrated ability to effectively communicate with executive-level management on a regular basis.
- Familiarity with data quality and governance issues and requirements.
- Expert knowledge of data management systems, practices and standards.
- Thorough knowledge of relevant rules and regulations.
- Demonstrated ability to work with others from diverse backgrounds. Demonstrated effective communication and interpersonal skills. Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization.
- Self-motivated and works independently and as part of a team. Demonstrates problem-solving skills. Able to learn effectively and meet deadlines. Strong organizational skills.
- Expert analytical and design skills, including the ability to abstract information requirements from real-world processes to understand information flows in computer systems.
- Ability to represent relevant information in abstract models. Critical thinking skills and attention to detail.
- 8 – 13 years of related experience.
- Solid understanding of machine learning models, data science methodologies, and generative AI concepts.
- Proficiency in programming languages like Python and experience with relevant AI/ML frameworks. Vertex AI experience is a plus.