Greater Louisville Project Searching for Data Fellow

January 20, 2021
, , , , , , ,

Applications for this position are no longer being accepted. You can join our mailing list to stay informed of future job opportunities at the Greater Louisville Project or other local organizations using data for good!

The Greater Louisville Project (GLP) is a Louisville nonprofit that creates data and research for local nonprofits, foundations, and civic leaders. You can check out our work here! We’re looking to hire a new team member to assist with crunching data, creating reports, and visualizing information for the community.

Duties

  • Compile data and visualizations for GLP reports and website from available sources
  • Conduct research for community data requests
  • Assist with other internal data projects
  • Visualize GLP data using appropriate tools

Required skills

  • Moderate fluency in R. If you aren’t familiar with R, you’ll need to show a strong background (project-based work or certification) in another computational language like Python that demonstrates skills transferable to R. We can evaluate this on a case-by-case basis.
  • Some background in data/statistics beyond just working with data in preformatted tables. The role involves working with survey data as well as some complicated data scraping and wrangling. Concepts like margins of error, survey weighting, medians, etc. come up often.
  • Show a commitment to service, justice, and equity. We create data to advance equity and make Louisville a better place for all, and you’ll need to be committed to those goals to engage with the work and be a good fit for the team.
  • Ability to learn new skills on the job with minimal supervision. We’ll provide appropriate support and mentorship, but data analysis also requires reading a lot of documentation and figuring out new things on your own.

Preferred skills

  • Community service and community engagement experience through your personal or professional life. Involvement in local organizations and social justice issues helps bring more community perspectives to this work, and it also helps us share our research. For example: helping folks access resources during COVID-19, work experience with other nonprofits, or involvement with a neighborhood organization.
  • Experience with data visualization and storytelling to people with a variety of technical expertise. Our goal is to make our research accessible to everyone in the community regardless of their background. 
  • While the role doesn’t require developing new methods for communicating our data, if you have ideas for how to better communicate our data through storytelling, graphs, dashboards, visuals, etc., you can make it happen!
  • Familiarity with any web languages and tools like HTML, CSS, WordPress, Google Analytics, D3. Doesn’t have to be a part of the job, but it can be if you’re familiar with them.
  • Familiarity with Python. We’re working on a specific project using Python, and it’s a good general-purpose skill to have when R isn’t the best choice.

Compensation + Opportunities for Advancement

  • Will be a contracted position, not an employee. We’re looking for someone to join for 15-30 hours a week, with flexibility depending on skill level and other commitments. An experienced R user would be fine with fewer hours, while someone transferring over to R from another language would need more time to get comfortable with the code.
  • Compensation starts at $15 if the applicant requires a lot of upskilling to start, and would go up to $20 with more experience. There will always be a reasonable gap between what someone would make at GLP and what they could make, say, joining the data team at Humana.
  • Potential for an apprenticeship model where the applicant graduates to a higher pay rate upon completion of certain milestones within their first several months. (Specifically this is in regard to someone who does not know R.)
  • Possibility for advancement over time as the applicant becomes familiar with GLP data systems and responsibilities.
  • Flexible start date, preferably sometime in early 2021.

APPLY NOW!

To apply, send resume to glp@greaterlouisvilleproject.org

Print Page

We’re Hiring a Data Fellow!

December 17, 2021

Child Care Is a Workforce Issue

April 28, 2021

Families can offset pandemic learning loss with simple strategies at home

April 28, 2021

Natural Breaks Algorithm

For each indicator, Greater Louisville Project assigns cities into one of three groups (high-performing, middle-of-the-pack, and low-performing) based on how they compare to other cities. The assignment is based on how cities naturally cluster on that indicator. Sometimes, the differences between cities are very small, and the difference between a city ranked 5th and 6th could simply be a matter of the sampling error that arises from using survey data. Thus, rather than always make a division that declares the top 5 to be the top tier, we use a natural breaks algorithm to look for a cluster of cities that is outperforming the rest, a cluster that is about average, and a cluster that is lagging. This clustering gives us a better indication of where Louisville is thriving and where Louisville has room to learn from cities that are doing better.

Z-Scores

Z-scores (or standardization) is a way to combine data with different units of measurement into a single index. The z-score is a measure of how far away a city (or census tract, etc.) is from the average city. In order to be comparable across different units of measurement, the z-score is the distance from the mean measured in standard deviations (e.g. if Louisville has a z-score of 1 it means Louisville is 1 standard deviation above the mean of its peer cities).

Data from the Robert Wood Johnson Foundation's County Health Rankings use z-scores and all z-scores are relative to the mean of Louisville's peer cities. (On the County Health Rankings site z-scores are relative to all the counties in each state - thus z-scores reported by GLP will be different, because we are using a different reference group). The Greater Louisville Project also uses z-scores in our multidimensional poverty index, which compares each census tract to the mean of all census tracts in Louisville.