# Data Science for Good: City of Los Angeles
**Help the City of Los Angeles to structure and analyze its job descriptions**

The City of Los Angeles faces a big hiring challenge: 1/3 of its 50,000 workers are eligible to retire by July of 2020. The city has partnered with Kaggle to create a competition to improve the job bulletins that will fill all those open positions.

# Problem Statement

The content, tone, and format of job bulletins can influence the quality of the applicant pool. Overly-specific job requirements may discourage diversity. The Los Angeles Mayor’s Office wants to reimagine the city’s job bulletins by using text analysis to identify needed improvements.  

The goal is to convert a folder full of plain-text job postings into a single structured CSV file and then to use this data to: (1) identify language that can negatively bias the pool of applicants; (2) improve the diversity and quality of the applicant pool; and/or (3) make it easier to determine which promotions are available to employees in each job class.

# How to Participate
1. Accept the Rules
 - Accept the [competition rules][1].  
2. Make Your Submission
 - Follow the [submission instructions][2].

WIth your help, Los Angeles will overcome a wave of retirements and fill those jobs with a strong and diverse workforce. Good luck and happy Kaggling!


  [1]: https://www.kaggle.com/c/data-science-for-good-city-of-los-angeles/rules
  [2]: https://www.kaggle.com/c/data-science-for-good-city-of-los-angeles/overview/submission-instructions



    Do you think companies can find better candidates by improving their job postings? We hope to create an open-sourced body of work focused on this topic by hosting another Data Science for Good competition, this time in partnership with the City of Los Angeles.