Research Scientist in Educational Data / Text Mining & Statistical Learning

Nexus Research and Policy Center
Institute for Teaching and Learning

The Nexus Research and Policy Center (Nexus) invites applications for the position of Research Scientist in its Institute for Teaching and Learning (ITL). This is a full-time research position at the postdoctoral level or above. The newly established Nexus Center is located in San Francisco, where the candidate will work in the company of other researchers and analysts exploring related fields. This is an opportunity to work with a group of dedicated professionals from a variety of backgrounds and to publish research findings in in-house reports and peer-reviewed journals.

As a part of the ITL, the successful candidate will help execute Nexus's annual research agenda examining the processes and effects of teaching and learning at both proprietary and traditional institutions of higher education.

Responsibilities:

• Extract, transform, and integrate data across multiple databases for quantitative analysis.
• Apply a variety of sophisticated statistical and data mining techniques to analyze large sets of data from existing databases, observations, interviews, surveys, and class artifacts.
• Develop and test data collection instruments.
• Continually review current literature on research and development on relevant algorithms and techniques that may inform and benefit ongoing and future research projects.
• Assist in designing, implementing, and communicating results of research projects consistent with the mission and research agenda of Nexus.
• Contribute to research proposals for grant programs and other funding opportunities.
• Supervise and mentor research assistants working on associated projects.
• Develop graphical and other representations to communicate findings of data mining.
• Contribute to the preparation of reports and presentations on research findings for both professional and lay audiences.
• Assist in developing a plan for publication and presentation of findings via scholarly journals and conferences.

• Graduate degree required (doctorate preferred) in computer science, informatics, statistics, artificial intelligence, cognitive science, human-computer interaction, or related field.
• Data extraction and transformation:
  • Ability to freehand complex SQL Statements and synthesize datasets from myriad different database systems.
  • Experience querying relational databases and a demonstrable ability to retrieve complex datasets from both the Oracle and SQL Server relational database management systems (RDBMS).
  • Demonstrated ability to interpret Entity Relationship Diagrams (ERDs).
  • Ability to analyze unstructured data and convert it into datasets suitable for statistical modeling and exploratory research. Ability to consolidate data across multiple sources, resolving data definitions, heterogeneity, and quality.
  • Knowledge of scripting languages such as Python, Perl, and PHP for processing large batches of text to facilitate subsequent data analysis.
  • Experience with Hadoop, Java, and C/C++ programming, including MapReduce algorithms.
• Data analysis:
  • Ability to analyze large datasets using SAS, SPSS, R, and other statistical software packages.
  • Facility with exploratory data analysis, multivariate linear and logistic regression, hypothesis testing.
  • Experience using multiple data mining and knowledge discovery techniques, such as clustering, decision trees, naïve Bayes, support vector machines, graphical models, and boosting, for both visualization and prediction.
  • Experience applying machine learning algorithms. Relevant areas of specialization include: natural language processing and text analytics, log file analysis, feature selection and optimization, pattern recognition, causal discovery.
  • Experience with basic text processing technologies such as dependency parsing, part-of-speech tagging, named entity recognition, and other work in information extraction and summarization. Familiarity with topic modeling technology, including Latent Dirichlet Allocation and Latent Semantic Analysis.
  • Experience with computational modeling. Particular areas of interest include cognitive modeling, intelligent educational systems, dynamic network analysis and visualization techniques.
  • Experience in designing and developing data mining and statistical learning algorithms for solving interdisciplinary problems.
• Research design and methodology:
  • Deep understanding of multiple qualitative and quantitative research methodologies, including large-scale survey research, experimental techniques, design research, observational and interview research, model comparison.
  • Proven track record in presenting and publishing original research in peer-reviewed conferences and journals.
• Strong communication skills:
  • Ability to coordinate interdepartmental communication to learn about systems/data, and shape that understanding into actionable requirements.
  • Ability to communicate complex and technical information to persons with non-research related backgrounds, including presenting research findings, reports, and recommendations to executive and senior management and other external parties.
• Interest and experience in applying computational skills to educational issues.

Dr. Norma Ming, Sr. Research Scientist, Institute of Teaching and Learning, Nexus Research and Policy Center, 199 Fremont Street, Suite 1400, San Francisco CA 94105

email: Norma.Ming@phoenix.edu