I am fascinated by Machine Learning (ML) and keep looking for case studies were ML solves real world problems. This Talk – Machine Learning: The Basics by Ron Bekkerman( video), provides a great overview of machine learning and how it is being used by LinkedIn for Job Analysis. LinkedIn is one of the early companies to jump in to Data Science. With over 200 million subscribers, they have ample data to analyze. The data is very contextual too and that helps build better algorithms (they claim 95% accuracy in prediction in a specific case). At one point in the talk Ron mentions that the ML study helped in building a product that generates about 6 million dollars in revenue for LinkedIn. That is great pay off.
Why is job analysis interesting in general? It provides you with some interesting insights into the direction a specific industry is moving:
- If you are in the (IT staffing) industry, you may want to know what kinds of jobs are in demand? And which ones are growing and which ones are shrinking?
- If you are an outsourcing company, you may want to analyze the hiring patterns in different parts of the world
- What kinds of skills are in demand for startups, medium sized companies and large enterprises? Lots of people from startups to training companies can use this data to build and tailor their offerings.
- How do training companies and conference organizers meet the need for skills using job analysis?
Ultimately, it is all Market Intelligence of a kind. It is fascinating that, now we have large data to analyze and get some glimpses into the patterns of demand/supply. So where do you get all this data from? That is a topic for another blog post.
One of our interns is working on an app to do Job Classification and automatic tagging of jobs. We were debating whether we should use some simple techniques or ML. I was going around looking for case studies and stumbled upon this video.