I have never heard a British person EVER call a bread roll a `pudding`.

We DO have arguments….mostly of a regional nature. I`ve heard bread rolls called both baps and barmcakes, for instance. But never, ever, a `pudding`. You are misinformed.

Or perhaps you are confusing the term with something else…dessert, afters, or whatever you call the sweet course in the US.

I have many times had a nice scone for pudding. `Pudding `being a common ( if now dated) term used for the second course. It is not the name of the confectionary itself, though, but an indication that it follows the main, usually savoury, course.

You then have the option to elaborate if you feel so inclined and/or if the now-former candidate asks either with the literal truth or something generic like “I just don’t think it’s a good fit.”

I actually had someone do that to me in a face-to-face and that’s how they worded it. It was supposed to be 3 steps in the interview and after 20 minutes they decided I wasn’t suited for them*. I was actually grateful that they chose not to waste my time going through the motions.

First of all, build a list of career coaches available in your area. Try to rank them according to critics, comments, availability … Then, meet some of them, for a first interview, to discy what you are looking for and how you can be helped.

You should look for people who have a high availability, and for experimented professionals. Ask them for references on similar coaching missions. Take the time to meet several of them – as you would do for any job interview.

The biggest piece of advice I could give is to take a course in microeconometrics/labour econometrics as a part of your course. If your course coordinator won’t let you, beg. If they still won’t let you, then go off-line for a week or two and properly digest Mostly Harmless Econometrics (or if your stats isn’t too good yet, Mastering Metrics). If you want to go and work in health analytics, then replace what I just wrote with the equivalent for research design.

Why learn microemet? Basically, many of the big questions in business are of the form “what will happen if we do x”. Predictive models that aren’t informed by causal reasoning do *terribly* at this question–they answer the question “what do we see happening to y when we see x”. Inferring what will happen to y when you fiddle with x is a difficult task when all your data come from a world in which you did not fiddle with x. Too often we come across people with great technical chops who aren’t even aware they’re making mistakes when answering these questions. Don’t be one of these people.

The second biggest piece of advice would be to not become too enamoured by the sexy end of data science (especially predictive algorithms), but *do spend the time learning this stuff in depth*. Often the simple stuff done well is far more useful to real-world decisionmaking.

Third: read very widely.