Full-Time Senior Research Associate
The Kellogg School of Management is seeking at least one Research Associate (or Senior Research Associate, based on qualifications) to join its growing Research Support organization. Your primary function in this role will be to enable Kellogg faculty to undertake and complete ambitious research projects.
Specifically, you will help us improve our methods for collecting, organizing, and delivering novel business datasets for analysis by researchers across the School. We anticipate you will help to solve increasingly complex challenges related to:
– Accelerating analysis of large volumes of data,
– Simplifying analysis of unstructured data, and
– Securing sensitive data.
As a successful candidate, you will bring diverse skills to meet these challenges. You should have a strong practical grasp of database design, information security, and high-performance computing concepts. You will need to empathize effectively with academic clients and demonstrate that you understand their needs.
This is a term position for up to three years, with an opportunity for renewal based on performance.
– Design databases and other tools for delivery of new research datasets.
– Update existing research datasets and verify their accuracy and completeness.
– Participate in the ideation, prototyping, and testing of new Big Data systems.
– Produce sample analysis code and other forms of documentation for new datasets.
– Train community members on subjects related to data management and analysis.
– May mentor and coach junior colleagues and Research Associates, as needed.
– PhD in a quantitative field, or equivalent terminal degree in their discipline.
– Advanced statistical programming skills in R, Matlab, SAS, or Stata.
– Prior experience designing and creating relational databases.
– Experience examining data critically for potential errors or inconsistencies.
– Strong data and code documentation habits.
– Outstanding communication skills with both technical and non-technical clients.
– Commitment to continual learning and development.
– Prior experience working efficiently with large scale (> 1 TB) datasets.
– Experience extracting data from distributed file systems.
– Strong understanding of applied statistics and computational social sciences techniques, including: logistic regression, structural equations models, constrained optimization, factor analysis, network graphs, sentiment classification, and machine learning techniques.
– Working familiarity with version control tools such as git.