We are searching for a Data Liaison and Analyst with strong Stata skills who can oversee all the quantitative data management on 2 to 3 large PK-12 education policy research projects. Projects involve working directly with local Colorado school districts (e.g., one randomized control trial in preschool settings; one collaborative research project with local school districts on teacher labor market topics). The position is initially a one-year, soft-money position that has the strong potential for additional years of work, if desired, pending successful grants. The successful candidate will report directly to project PI’s to oversee the data acquisition process directly from the district(s), will manage those raw data files, and will be primarily responsible for writing code to prepare those datasets for analysis (e.g., difficult data merging tasks, reshaping, ID linking, data cleaning, data quality checks, document final cleaned datasets). If desired, candidates may develop leadership opportunities on these projects, however this is not a requirement.
This position would be best suited for an individual who is not focused on accruing publications in the very short term. The position could be a good fit for a strong master's student who has recently graduated. We also welcome applicants with a relevant PhD who are interested in participating in quantitative research in a role outside the traditional tenure-track professor role but wish to remain connected to an academic setting. Preference will be given to candidates who commit to work 50 - 100% time and are interested in relocating to Boulder in order to be physically present in the research lab. However, if the strongest applicant can only work part-time and/or remotely, the position can be modified to accommodate those preferences.
Primary Role Responsibilities:
- Make project-specific data intake plans/ timelines to share with external partners
- Oversee data intake from providers (e.g., districts, states, federal datasets, educational organizations)
- Conduct and document initial quality control checks on new data received
- Set up systems for center projects to meet data security and collaboration needs
- Manage data cleaning and architecture on specific projects, including producing high-quality, well-documented, easy-to-read Stata code to transform raw data received into clean, generated datasets designed to support analyses.
- Support project computing resource needs for data processing
- Respond to short requests for simple descriptive analyses (with guidance)
- Maintain data relationships with external partners (e.g., update MOU's, maintain compliance with data sharing agreements)
Optional Role Opportunities (as desired by candidate):
- If the person also has strong interpersonal/ management skills, candidate can also participate in greater leadership and coordination role on research projects.
- The opportunity to be a part of our Research & Evaluation Methods (REM) community, including attending weekly seminars, providing guidance to doctoral students and perhaps even deepening their Stata skills.
- The opportunity to teach and train members of the REM community on new data processing advancements (e.g., writing ado programs, github, etc)
Start Date and Duration:
The data analyst would start as soon as possible. Candidates must commit to the role for a minimum of one year and a minimum of 50% time (20 hours per week). There is a strong possibility to extend the position for additional years, if desired. The position could be modified to accommodate a part-time (50 or 75% time) or full time schedule. We will consider applicants both able and unable to relocate.
- Most important: Strong Stata coding skills (someone who can think clearly about cleaning, merging, reshaping, appending, etc.). The Stata coding principles described in J. Scott Long’s book, “Workflow of Data Analysis Using Stata” provide a useful way of thinking about these skills.
- Strong data analysis skills when given clear guidance.
- Very organized, detail-oriented, responsible.
- Experience working on quantitative research projects with primary or secondary data
- Relevant master's degree (e.g., education, public policy) or PhD. Can be a recent graduate.