Post by High Priestess on Jul 9, 2016 14:07:58 GMT
See the article:
www.latimes.com/business/technology/la-fi-himi-grewal-snap-story.html
Airbnb’s data science work team touches every part of the company’s platform, Grewal said. For example, if the data science team sees that lots of people are searching for lodging in a particular city but aren’t actually booking anything, then perhaps they’re not finding enough homes available for rent, and the company should invest resources into growing that market. “We’re basically interpreters,” Grewal said, “we distill the voice of our customers and use that voice to help inform our products.”
Diversity with data: Grewal’s team has used data science to help diversify the company’s predominantly male workforce. To figure out why so few women were being hired on to the data science division, the team identified the point at which many female applicants dropped out of the recruitment process: the take-home exercise. “We took a close look at this and realized that the people who were grading the take home exercise had no clear rubric, so we changed this and made it clear what we were looking for, we made the grading consistent, and if a person was successful they were moved to the next round.” The percentage of women on the data science team has since doubled to 30%.
www.latimes.com/business/technology/la-fi-himi-grewal-snap-story.html
Airbnb’s data science work team touches every part of the company’s platform, Grewal said. For example, if the data science team sees that lots of people are searching for lodging in a particular city but aren’t actually booking anything, then perhaps they’re not finding enough homes available for rent, and the company should invest resources into growing that market. “We’re basically interpreters,” Grewal said, “we distill the voice of our customers and use that voice to help inform our products.”
Diversity with data: Grewal’s team has used data science to help diversify the company’s predominantly male workforce. To figure out why so few women were being hired on to the data science division, the team identified the point at which many female applicants dropped out of the recruitment process: the take-home exercise. “We took a close look at this and realized that the people who were grading the take home exercise had no clear rubric, so we changed this and made it clear what we were looking for, we made the grading consistent, and if a person was successful they were moved to the next round.” The percentage of women on the data science team has since doubled to 30%.