Full-Time Senior Computational Research Consultant
The Northwestern University Kellogg School of Management has a unique opportunity for a mid career professional who is interested in using their scientific computing and data science experiences in an academic setting. As a Senior Computational Research Consultant (SCRC), you will draw on a broad set of technological, creative, and problem-solving skills in order to accelerate and facilitate ambitious academic research.
In this role, you will have a chance to work with many different research on many different kinds of data problems. Some of the challenges from recent projects have included: natural language processing, topic modeling, machine learning, web scraping, parallel processing, and large scale datasets. On our team, you can expect to work with bright minds on challenging problems, evaluate emerging technologies, and keep learning new techniques to solve research problems. As these technologies and research questions evolve, so will your role.
- Listen critically and effectively in order to understand our faculty’s research needs.
- Conceive, design, prototype, and implement innovative solutions in order to collect, transform, analyze, and visualize unique social science data.
- Encourage our researchers to adopt innovative new technological tools and methods by developing training materials, guides, and other resources.
- Provide training and consulting for conducting statistical analyses using standard software and tools.
- Create and update documentation on resources, tools and data management practices.
- May mentor and coach junior colleagues and Research Fellows, as needed.
- Advanced degree (M.A. or M.S. minimum, Ph.D preferred) with a research component in any quantitative field.
- At least 4 years experience in roles that require working with data, performing academic research, and/or writing code.
- 3-5 years experience using at least two of these languages for statistical programming: Matlab, R, Python, SAS, and/or Stata.
- 1-2 years experience with Unix/Linux, including scripting.
- Strong understanding of applied statistics methods, particularly as they apply to computational social sciences: logistic regression, structural equations models, constrained optimization, factor analysis, network graphs, sentiment classification, and machine learning techniques.
- Ability to organize and document datasets intelligently, and to examine data critically for potential errors or inconsistencies.
- Ability to work independently and manage time effectively in an environment with unpredictable work-flows.
- Outstanding communication skills with both technical and non-technical clients.
- Commitment to continual learning and development.
- Experience writing regular expressions rules or using natural language processing (NLP) algorithms to extract information from poorly structured data sources.
- Experience extracting data from distributed file systems using tools such as MapReduce, Hive, Spark, or Pig.
- Ability to work efficiently with large datasets using scripting languages and/or statistical software packages (Python, R, SAS or similar).
- Hands-on experience with designing or setting up computing systems for high performance.