Sr. Data Scientist

Job Description

If you're passionate about building a better future for individuals, communities, and our country—and you're committed to working hard to play your part in building that future—consider WGU as the next step in your career.

Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.

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:
Grade:
Technical 410 Pay Range: $ - $

• Documents data, analytics, and research needs in projects of high complexity with a student and equity-centered lens, collaborating with peers, cross-functional partners, faculty staff, and leaders. Leads the translation of user stories into technical requirements.
• Sets and manages expectations about complex analytics tasks and activities through clear, timely, and effective communication with partners and stakeholders.
• Answers complex business questions requiring extensive knowledge of the university's data assets across several domains and departments.
• Identifies adequate data sources and data sets to evaluate hypotheses and produce forecasts.
• Analyzes very large data sets from both structured and unstructured sources and develops statistical and predictive models.
• Collaborates with Data Engineering in the development of complex ETL/ELT processes and data pipelines.
• Identifies, investigates, and solves complex data issues, contributing to the accuracy, completeness, consistency, timeliness, and validity of the university's data.
• Collaborates with Data Engineering and other data & analytics partners to define standards and best practices that increase data quality across the university.
• Utilizes software, scripts, and algorithms to perform complex data-related tasks (e.g., importing, cleaning, transforming, analyzing, displaying) without human intervention.
• Combines data analysis, visualization, and narrative structures to convey information in compelling ways that instigate deliberate action.
• Conveys information effectively to peers, partners, and senior leaders, using a variety of resources and formats (synchronous and asynchronous, verbal and written) such as e‑mails, presentations, meetings, and workshops.
• Creates and organizes information about processes, projects, operations, data assets, and insights from analyses and research, making it accessible in ways that increase the university's knowledge and efficiency.
• Writes and interprets technical documentation (e.g., Entity-Relationship, Conceptual, Logical, and Physical data models).
• Contributes actively to the development of the university's data management platforms (e.g., data dictionaries, catalogs, etc.).
• Plays a prominent role in other team members' development through constructive feedback and sharing of technical and institutional knowledge.
• Tracks and reports own progress, dependencies, and challenges diligently. Breaks down complex goals into concrete tasks and activities, and actively supports leaders in project planning.
• Drives tasks, activities, and medium-scale projects with high levels of autonomy, confidence, and collaboration with peers and partners.
• Designs medium-scale solutions and collaborates effectively with other technical specialists (e.g., data engineers) in the construction of data products, systems, and applications.
• Supports…