Current WGU Employees must complete an internal application before 1/7/2021 to be considered for this position.
The Data Scientist is skilled at discovering actionable insights from multiple, large and complex data sets. The data scientist conducts high-quality quantitative research to inform our learning design efforts and software systems that drive real-world impact on learners.
The successful candidate excels at research techniques underpinning learning science, such as predictive modeling, clustering, recommendation engines, and personalization. They also push the boundaries of the field, seeking new and innovative ways to extract meaning from digital data of all types.
Essential Functions and Responsibilities:
Proposes and conducts quantitative research, discovering insights into student learning and educational attainment and suggesting areas for research and development investment. Mines large data sets and creates predictive models employing traditional statistical techniques and machine learning algorithms. Communicates research findings to lay and technical audiences. Creates reports, visualizations, and other supporting artifacts for internal- and external-facing publication. Interfaces with internal teams and external partners in the definition and execution of research projects. Develops and maintains a collaborative relationship with all appropriate internal and external stakeholders. Stays current with trends and emerging techniques in data science, learning science, and related fields. Fulfills other duties as assigned.
Knowledge, Skill, and Abilities
- Experience as a data scientist conducting quantitative research in a behavioral science context, preferably related to learning
- Able to conduct data mining and modeling activities and communicate insights and opportunities to stakeholders.
- Given very large data sets, proven experience setting up and maintaining big data processes, big data environments, and efficient processing of large amounts of unstructured data.
- Proven skills working with many types of structured, unstructured, and semi-structured data using tools such as R, Python, SQL, SAS, and Scala within big data environments such as Databricks and Hive.
- Proven experience leveraging several of the following techniques:
- logistic regression, factor analysis, classification algorithms, tree models, etc.
- machine learning algorithms such as neural networks, decision trees, association rules, and unsupervised learning methods
- Good written, oral, presentation, and interpersonal skills.
- Ability to work in a collaborative team and maintain effective relationships.
- Bachelor’s degree or higher in a relevant field such as Statistics, Econometrics, or Learning Science
- 2+ years conducting quantitative research with very large data sets or big data
- Masters preferred
- Learning science background preferred
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