Quick LinkedIn data algorithm software

Quick LinkedIn data algorithm software brief I need a quick software to help can pull out data from LinkedIn and calculate the number of people that got hire at a selected company in the last 3 years that come from outside the industry. The challenges are: • LinkedIn capabilities don’t allow to such a search for people that currently work in one company and industry but were in another industry before. • Some people might have selected their company but the industry might not be correct. • Some companies have more than one name in Linkein so we need to merge them in the county. Using LinkedIn public data we need to overcome the above challenge and to find out: 1. How many people working at company < Xi> in Country <Yi> have worked inside the Wine & Spirits / Alcohol Beverage industry in Country <Yi> before hired a. Actual number and % of the total employees of that company in that country. 2. How many people working at company < Xi> in Country <Yi> have worked inside the Wine & Spirits / Alcohol Beverage industry in any country before hired; a. Actual number and % of the total employees of that company in that country Yi. 3. How many of the people who joined Company <Xi> in Country <Yi> in the last 3 years, come from outside the Wine & Spirits / Alcohol Beverage industry in Country <Yi> on their immediately prior job a. Actual number and % of the total number of people hired in the last 3 years 4. How many of the people who joined Company <Xi> in Country <Yi> in the last 3 years, don’t come from a Wine & Spirits / Alcohol Beverage company right before hired, but worked in the industry at some point before a. Actual number and % of the total number of people hired in the last 3 years 5. How many of the people who joined Company <Xi> in Country <Yi> in the last 3 years, don’t come from any Wine & Spirits / Alcohol Beverage experience at any time in their career a. Actual number and % of the total number of people hired in the last 3 years 6. How many of the people who have “Global” on their current title at Company <Xi> OUTSIDE Country <Yi> don’t have Wine & Spirit / Alcohol Beverage experience in Country <Yi>. a. Actual number and % of the total number of people with Global title in Company XiYi 7. How many of the people who have “Global” on their current title at Company <Xi> don’t have Wine & Spirit / Alcohol Beverage experience before. a. Actual number and % of the total number of people with Global title in Company XiYi FOR ALL THE ABOVE, we need to see to be able to filter (or have subtotals) by work level: • Manager, • Director • VP • SVP • CEO • CFO • COO • CMO • Board • All levels FOR ALL THE ABOVE, we need to see to be able to filter (or have subtotals) by functions: • Marketing • Finance • Human Resources • IT • Logistic • Legal and Regulatory • Supply Chain • Operations • General Management / President • All functions FOR ALL THE ABOVE, we need to be able to see the data by all possible XiYi combinations and all SumXi in Yi, and Xi in all Sum Yi. (ie: All Diago hires in <country> that….. All Diageo hires in all countries analyzed that…. All the selected companies in <country> that…. ) FOR ALL THE ABOVE, we also want to be able to group and/or add countries and companies into the analysis when we get the first read without having to start all the from scratch See Attachment for Word Document Version of the Brief including for list of Companies (73) and Countries (41).