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Summary
The Data Scientist is responsible for producing valuable information from very large sets of structured, semi-structured, and unstructured data. They establish and maintain strong relationships with peers and leaders across IAR, Data Engineering, Product Management, Finance, EdTech, and Faculty staff. They utilize statistical models, machine learning, text mining, natural language processing, and other methods to create predictive models. These models may be integrated into new or existing workflows.

Job Duties

• Documents data and analytics needs in projects of medium/high complexity with a student and equity-centered lens, collaborating with peers, cross-functional partners, faculty staff, and leaders. Translates user stories into technical requirements.
• Sets and manages expectations about analytics tasks and activities through clear, timely, and effective communication with partners and stakeholders.
• Identifies adequate data sources and data sets to evaluate hypotheses and produce forecasts.
• Analyzes large data sets from both structured and unstructured sources and develops statistical and predictive models.
• Collaborates with Data Engineering in the development of 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.
• 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.
• Selects and deploys a variety of mathematical models, forecasting methods (e.g., time series, inferential, regression), descriptive statistics, and/or classification algorithms to make predictions and identify relationships based on limited sets of data.
• 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, meeting, and workshops.
• Creates and organizes information about processes, projects, operations, data assets, and insights from analyses, 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 enrichment of the university’s data management platforms (e.g., data dictionaries, catalogs, etc.).
• Tracks and reports own progress, dependencies, and challenges diligently.
• Designs small-scale solutions and collaborates effectively with other technical specialists (e.g., data engineers) in the construction of data products, systems, and applications.
• Understands and abides by the relevant policies and methods to access, use, transform, store, and delete data in responsible, secure, and compliant ways.
• Works actively in improving own skills and knowledge through internal and external, formal and informal, structured and unstructured learning. Is a lifelong learner and embodies a growth mindset.
• Performs other job-related duties as assigned.

KSAs

• Advanced SQL proficiency, with experience writing queries and subqueries, modifying data (INSERT, UPDATE, DELETE), creating views, and knowledge of the different join types, filtering, sorting, aggregation, window functions, common table expressions (CTE), and performance tuning.
• Ability to interpret and design models that describe how data relate to one another and to the properties of the real-world entities they represent.
• Familiarized with versioning tools in the context of CI/CD (e.g., Github) and deploys models using the university’s machine learning operations (MLOps) self-service capabilities.
• Highly proficient and experienced in using tools like Tableau and Power BI to present data and information utilizing charts, graphs, and maps, in ways that make it easy to understand trends, patterns, and outliers.
• Experience using Python, R, and/or Natural Language Processing in an ML environment, requiring minimal supervision.
• Skilled at selecting and deploying methods and techniques like A/B Testing.
• Comfortable with common project management methodologies and frameworks (e.g., Waterfall, Agile, SDLC).
• Proficient in the MS office suite, including advanced Excel knowledge.
• Proficient in flowchart and diagramming tools like Miro, Visio, Lucidchart, and similar applications.
• Ability to apply sound judgment, systems-thinking, and analytical skills to assess risks, perform root-cause analyses, make recommendations, and drive decisions that contribute to the achievement of the university’s objectives.
• Ability to perform assigned tasks with high levels of autonomy, reliability, self-direction, and with a bias for action. Manages conflicting and concurrent activities with sporadic support from their leader(s).
• Knowledgeable of the university’s most relevant KPIs and the drivers that affect them.

Minimum Qualifications

• Bachelor's Degree in a related discipline
• 3 years of related experience in Data Analysis, Business Intelligence, Data Science, Statistics, Decision Intelligence, Research, Learning Science, or Behavioral/Cognitive Psychology.
• 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.

Job Description Disclaimer: This position description provides the major duties/responsibilities, requirements and working conditions for the position. It is intended to be an accurate reflection of the current position, however management reserves the right to revise or change as necessary to meet organizational needs. Other responsibilities may be assigned when circumstances require.

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:

How to apply: apply online

Full-time Regular Positions (FT classification, standard working hours = 40)

This is a full-time, regular position that is eligible for bonuses; medical, dental, vision, telehealth and mental healthcare; health savings account and flexible spending account; basic and voluntary life insurance; disability coverage; accident, critical illness and hospital indemnity supplemental coverages; legal and identity theft coverage; retirement savings plan; wellbeing program; discounted WGU tuition; and flexible paid time off for rest and relaxation with no need for accrual, flexible paid sick time with no need for accrual, 11 paid holidays, and other paid leaves, including up to 12 weeks of parental leave.

The University is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.