PROJECT HUSH RECRUITER

• linkedIn api response structure:

a = [{'entityUrn': '', 'profile': { 'summary': '', 'industryName': '', 'currentLocation': '', 'student': None, 'headline': '' }, 'education': [ {'schoolName': '', 'startDate_month': None, 'startDate_year': None, 'endDate_month': None, 'endDate_year': None}, {'schoolName': '', 'startDate_month': None, 'startDate_year': None, 'endDate_month': None, 'endDate_year': None} ], 'projectView': [ { 'title': '', 'description': '', 'url': None}, { 'title': '', 'description': '', 'url': None}, { 'title': '', 'description': '', 'url': None} ], 'skillView': [ {'name': 'Python (Programming Language)'} ]} ]

Guidelines for data extraction

1. Select what all fields you are capturing.
2. Try to convert all these fields into numeric data (think of scoring these fields on a scale of 0-10)
3. A rule can be defined to calculate the scores of every field
4. Naming conventions :
   • all field name should be lowercase , ' ' is separated by '_'
   • If you can produce multiple subfields from a single field then follow naming convention -> <field_name>_<subfield_name>

• PSUEDO code for extracting data from public apis:

  • LinkedIn: viewProfile API Field - email (apply regex in summary) Field - Firstname Field - Lastname Field - text_data_headline Field - text_data_summary Field - text_data_work_descriptions - fetch latest description Field - numeric_data_work_experience *(only job role): rule_1 : extract the job role, then score +=add(match(job role, input job role) -> percent, years of experience ) skillCategory API Field - numeric_data_skills: skillCategory API - elements/[list of skill categories/endorsedSkills/[list of skills]/skill.name, endorsementCount, if insights then insights.insightText.text] rule_1 : skill_system_design has value 5 by default , rule_2 : if this skill is endorsed then +1 , rule_3 : if skill has linkedin skill assessment then +2 posts api Field - text_data_posts (top 5) Field - numeric_data_posts: logic : to filter relevant posts rule_1: fetch likes count for the relevant posts certifications api Field - numeric_data_certification: if timePeriod then if isIfRecent(months=3) then consider for scoring **optional -- check if the account activity isRecent StackOverflow: scores per tags badges per tags Number of answers reputation number of upvotes in each answer in top post

  • Github:

    • of stars in each repo
    • of contibutions
    • of forks
    • achievements(badges)

Algorithm :

score_solution_architect = field_1 * weight_field_1 + .... + field_n * weight_field_n weight_field_* will be defined manually, its not necessary that all fields have to be defined in case a field is not defined then it will be 0

Algorithm for merging the data from different source :

• check if reference of the other source is defined in a source
• based on first name and last name (calculate similarity % between first name and last name)

Python packaging

• python -m build in root directory
• twine upload dist/* in root directory

Docker commands used:

• docker build -t hushrecruiterimage .
• docker run --env-file ./env_variables.list hushrecruiterimage make sure to fill the access token details in the .list file
• Tagging the image : docker tag hushrecruiterimage prabhupad26/big-data-prog-sol:hushrecruiterimage
• Pushing the tagged image : docker push prabhupad26/big-data-prog-sol:hushrecruiterimage

Sending test link to the selected candidates via e-mail

Once candidates are selected through an algorithm, selected candidates receive a test link via mail. The test link contains 3 coding questions which need to be submitted within a specified time, otherwise the link will expire.