Director, Data Science

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The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:

Job Description

• Develops and implements an integrated analytics strategy, including decision models, simulations, and recommender systems, to enhance faculty decision-making, reduce decision fatigue, and support personalized academic journeys, while aligning with institutional goals and stakeholder needs.
• Owns a strategic roadmap for applying AI/ML and simulation-based tools to support continuous improvement of decision intelligence recommendations that support faculty and other student-facing roles and reduce administrative burden.
• Translates institutional priorities into intelligent systems that anticipate faculty needs and streamline outreach to students.
• Acts as a thought leader in applied AI, shaping how emerging technologies are leveraged to enable data-informed actions across academic operations.
• Collaborates with business partners to define and track objectives and success metrics that assess the real-world impact of model recommendations on faculty productivity and student outcomes.
• Leads the design, development, and production deployment of machine learning models that support timely and personalized faculty interventions.
• Coordinates with product, engineering, and analytics teams to ensure model outputs are dynamically integrated into other products, tools, or algorithms that depend on real-time data and recommendations.
• Partners with Product, Business, and MLOps teams to embed models into tools and workflows that reduce the need for manual dashboard review and drive efficient, data-backed actions. Ensures that models are up and running when integrated into workflows.
• Partners with faculty and operational teams to capture behavioral data and feedback on model-driven decisions, enabling continuous model refinement and performance improvement.
• Leads, mentors, and develops a high-performing team of data scientists and analysts, cultivating technical excellence and ownership. Actively attracts, retains, and develops top talent to ensure long-term team strength and capability.
• Fosters a culture of experimentation, delivery, and collaboration that balances cutting-edge innovation with business value.
• Works closely with other analytics and data teams to share knowledge and maintain alignment on best practices and institutional goals.
• Serves as a proactive partner to Product, Business, and Engineering counterparts, helping define opportunities where data science can enable smarter, faster decisions.
  Sets and manages expectations on timelines, technical feasibility, and trade-offs-ensuring transparency while maintaining focus on delivery.
• Engages non-technical stakeholders with clarity and insight, translating complex solutions into actionable ideas and facilitating adoption.
• Identifies key data requirements to support model development and insight generation, aligned to high-impact business problems.
• Collaborates with Data Engineering to ensure pipelines and instrumentation are in place to capture the right data at the right time, at the right quality.
• Works with internal and external partners to augment datasets as needed and ensure relevant data is accessible within shared platforms.
• Keeps stakeholders consistently informed about project goals, timelines, milestones, and risks through clear, concise, and timely updates.
• Adjusts messaging and presentation style based on the audience, translating complex technical concepts into accessible insights for non-technical partners.
• Collaborates closely with other analytics, data science, engineering, and product teams to ensure alignment, share knowledge, and coordinate delivery across interconnected initiatives.
• Stays at the forefront of trends in machine learning, applied AI, and educational analytics, evaluating new methods that can improve how decisions are supported.
• Leads experimentation efforts to test, iterate, and improve models and user experiences, ensuring tools evolve alongside faculty needs.
• Encourages curiosity, creativity, and long-term thinking in how the team approaches complex academic and operational challenges.
• Performs other job-related duties as assigned.

This job description includes a general representation of job requirements rather than a comprehensive inventory of all required responsibilities or work activities. The contents of this document or related job requirements may change at any time with or without notice.

Qualifications

• Deep expertise in applied machine learning, statistical modeling, and simulations, with hands-on experience deploying models in production environments to drive real-time decision support and automation.
• Advanced proficiency in Python and SQL, with strong working knowledge of cloud-based data platforms and familiarity with MLOps practices for model deployment and maintenance.
• Strong background in predictive modeling, natural language processing (NLP), and experimentation methods.
• Demonstrated ability to design and lead data science initiatives at the intersection of analytics, product, and engineering, translating complex challenges into scalable solutions.
• Proven experience building and leading high-performing, cross-functional teams; capable of guiding teams through change, scaling operations, and cultivating a culture of innovation and accountability.
• Track record of developing and executing forward-looking analytics strategies aligned with institutional priorities, including driving adoption of AI-driven systems and tools.
• Strong project management skills, with the ability to oversee multiple complex initiatives and ensure high-quality delivery within scope and timelines.
• Skilled in navigating organizational complexity and leading large-scale transformation initiatives related to data systems, team structures, and institutional processes.
• Excellent communication and interpersonal skills, with the ability to engage and influence senior leaders, collaborate across technical and non-technical teams, and foster data literacy.
• Experience managing budgets, vendor relationships, and resource allocation to ensure cost-effective delivery and alignment with business value.

• Master's degree in a related field (data science, analytics, computer science, etc.).

Experience

• 10+ years of experience in data analytics, data science, or a related field.
• 5+ years in leadership roles overseeing large, cross-functional teams.
• Extensive experience collaborating with product and data engineering teams.

Experience in lieu of education

Equivalent relevant experience performing the essential functions of this job may substitute for education degree requirements. Generally, equivalent relevant experience is defined as 1 year of experience for 1 year of education and is the discretion of the hiring manager.

Preferred Qualifications

• 3+ years managing managers.
• Experience managing large budgets, including oversight of vendor contracts and resource allocation for analytics initiatives.
• Strong understanding of decision intelligence principles and platforms.

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Position & Application Details

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.