- We were able to interview several recruiters as to what hampers their productivity in finding the talent required. Several usual variables surfaced including finding unable to find contact emails, understanding the ever changing technology, clarity from business and other similar variables. (will do a separate email on this next week). But one thing that hit me was understanding the levels of the jobs in the target companies and whom to target for your need. Let me explain this with an example. Suppose you are interested in hiring a Machine Learning Engineer in Google.
- Google has different flavors of ML jobs
- Software Engineer – Machine Learning
- Data Scientist
- Research Scientist – Google AI
- Principal Engineer – ML – Cloud Infrastructure
There are also some super specialized roles such as Augmented Reality and Virtual Reality on top of these roles but for our analysis in this email let us not get into those. So if you an enterprise recruiter who have the mandate to bring ML people from a company like Google (assuming the instance you have the mandate to bring talent from top companies) where do you look?. Let me try and unpack this a bit further
Typically in Google this falls under their L3 and L4 levels where different fresh ideas are brought into testing and developing models. This is the core layer of Machine Learning
Typically collaborates across products and more focused on optimization and product performance. (L5 and L6 levels in a company like Google
This is more a scaling job. How to improve the systems so they work in really large scale environment?
This is really a strategic job in Google – Product definition, Resource Provisioning and other similar activities
A typical enterprise getting into Machine Learning really have not much use beyond Software Engineer – Machine Learning. So in a scenario where you are targeting to hire from a peer understanding this dynamic is extremely crucial and saves a lot of time for the Recruiters. I picked Google here but this dynamic exists across all the peers from where you hire. This will mean significant time savings. We can do this type of analysis for your peers across job families
- Another initiative we have launched is to study which company is pushing the boundaries with respect to Digital Transformation. For this, we are studying the quality of Engineering workforce. This is an ongoing effort but some initial analysis shows that Target Corporation is really setting new standards in Big data and AI teams. Globally Target has well over 450 members in their AI team!. Pepsico has well over 280 people and they actually beat Coca-Cola in this regard who have about 180 people. They are doing several interesting use cases including price automation, store behavior analytics and various interesting digital intentions.
- We have evolved our models to look at feeder talent pool. We are planning to represent in a simple way. Suppose you want to hire Scala in Dallas which is an advanced skill. Logically the next best skills can be sized and delivered to Recruiters like this. This is something we are internally calling it as “Skills Pipe”. For example, if you are hiring Scala in Dallas there are 1600 people with that skill but the next closer skill Python there are 17K professionals. If you are ready to reskill from Java then the talent pool jumps to 36,000. This gives you better planning capability and our Simulation module will let you do that automatically in the next few months.