There’s no shortage of great talent for companies such as Apple, Google, Intel, Facebook, Wikipedia and some exciting startups. But what if you are not one of these?
I received the following job ad in my mailbox (see below in italics), from a third-party recruiter, and it’s probably for a data science position at Nike near Portland, Oregon (my guess). Basically, it’s a 6-month gig to build an A/B platform.
I discuss here a few aspects that make this job ad unlikely to attract talent, as well as remedies.
This skills mix is not found in typical employees. Granted, this is a consulting job, but you should then advertise it as a consulting opportunity. You would have to offer $300/hr to motivate the few guys that have this rare mix of talent and knowledge. There are consultants or small consulting companies (with this mix) willing to do the job for $300/hour (motivation is even bigger if you reimburse their weekly hotel/travel expenses, and consulting firms are better equipped than individual consultants as they can use a mix of cheap, junior with expensive, senior consultants to lower the cost). Or do you really need to build such a platform in the first place? Why not get one from a vendor instead?
You mention that machine learning / statistics is a plus (not a requirement), yet the core of the job is developing an A/B testing platform. Something where deep statistical knowledge is most critical.
For a regular employee, you would want an engineer with some statistical knowledge, train him so that he becomes an expert in experimental design (aka A/B testing) – at least in digital experimental design. The guy needs to acquire a deep knowledge of Internet/server architecture, traffic flows and web metrics, to understand source of biases in this type of A/B testing. Yet he needs to be very knowledgeable about statistical theory and non-parametric, robust confidence intervals in the context of big data and most importantly – in the context of doing TONS of A/B tests, whether the data is big or small.
In short, you need someone with some very specialized, narrow (not comprehensive) statistical knowledge (not an expert in logistic regression) and very specialized (but not comprehensive) knowledge about Internet architecture.