Educational Data Scientist
New York University
This position is not available for remote work - you must work onsite Monday-Friday.
The educational data scientist will support the service goals of NYU Research and Outcomes Assessment. The Research and Outcomes Assessment team's core roles are to lead, support and evaluate strategic learning and teaching projects via the measurement, collection, analysis and reporting of data.
The Educational Data Scientist will lead the implementation of data capturing and reporting systems. These activities will include identifying data requirements, managing the data gathering and transformation process, analysing and identifying trends in complex data sets, and developing graphs and data dashboards for reporting purposes.
The successful applicant will bring project management, systems knowledge and skills and software engineering expertise to acquire, manage, manipulate and analyse data and report results for University-wide strategic projects aimed at improving students' experience of learning and the quality of teaching.
The educational data scientist is someone who: wants to know what the question should be; embodies a combination of curiosity, data gathering skills, statistical and modelling expertise and strong communication skills. The educational data scientist must have a combination of statistics, computer science and information design, good communication and collaboration skills.
One important basis for working with big data is pattern recognition in noisy data, which is a very visual. One focus of educational data science will be on developing animated, multi-dimensional techniques for mining and visualising data. The educational data scientist must dive from Learning Analytics (LA) into Educational Data Mining (EDM) and resurface: exploring the real world, proposing meaningful measures, modelling the data, visualising the output, sharing the technique and automating the process.
The educational data scientist will have an understanding of the cognitive, contextual and design aspects of the transactions that generate educational data and the interdisciplinary sciences that will contribute to an understanding of it.
In-depth knowledge and application of educational data mining, with a focus on the provision of learning and teaching support for students and academic staff, will be important to succeed in this role. Knowledge of data warehousing technologies and data mining computational methods will also be essential, together with excellent analytical and problem solving skills.
We seek someone who excels in data management, statistical, analytical, visual presentation and written communication skills in a higher education environment, and has the ability to work collaboratively with faculty, learners, and staff. We highly value self-motivation and independence as well as creativity.
TASKS, DUTIES, AND RESPONSIBILITIES
- Applying data analysis techniques including data mining and the development of predictive analytics models.
- Use a combination of statistics, computer science and information design, good communication and collaboration skills.
- Engaging with faculty, instructors, students, and staff to identify and facilitate the use of analytics to support teaching and learning
- Collaborate in the design and implementation of learning analytic plans with faculty and instructional technologist to conduct formative and summative assessment of student learning through the delivery of their courses
Collaboration with technically fluent as well as novice faculty, and staff, on wide range of projects.
Assist with the supervising, training, and evaluation of part-time student assistants and project consultants. Supervise a graduate assistant.
M.A. Educational Data Mining or Learning Science & Technology
Ph.D. Educational Data Mining or Learning Science & Technology
SPSS, R, Shiny, RStudio, EventFlow, Text Analysis Software, Tableau or other statistical packages required for specific analysis
Demonstrated expertise in …
Using various forms of Learning Analytics such as SNA, NLP, EDM, Predictive Analytics, Adaptive Analytics, Multimodal Learning Analytics
Using data visualization software such as Tableau and R
Working with sensitive, personal or protected data
Navigating Learning Management Systems (especially Sakai)
Engaging with Higher Education Student Information Systems