Tips for Hiring a Data Scientist into a Tech Company • Hacker News Board

Re: Tips for Hiring a Data Scientist into a Tech Company

#21

Post by scottlocklin » Sat, Jan 11, 2020, 11:35 PM UTC

This is a meaningless checklist; the only actual useful advice is to get the word out at meetups and such. The way you hire a data scientist is much the way you hire into any other team.

If you have a data scientist who is worth anything, he should be given responsibility for building the DS team. If you don't: you probably shouldn't hire one. If you must: hire one with experience, if necessary, as a consultant to build your DS team.

Putting physicists at the top rank is also a bad piece of advice; people with experience are at the top rank for DS. If it's pure fresh meat, you're better off with applied math people than physics people (and I am physics people).

Re: Tips for Hiring a Data Scientist into a Tech Company

#22

Post by mygo » Sun, Jan 12, 2020, 3:10 AM UTC

tempsy wrote: post #9

I feel like pursuing Data Science as a career is such a career trap. Most companies vary wildly in expectation for these roles. You might be asked to spend most of time data engineering (like making ETL pipelines in one), or build a bunch of basic ML models , or spend all your time doing SQL queries and building dashboards in another. It’s silly and would never recommend anyone pursue that path as a lifelong career.…

You could say the same about recommending someone getting a computer science degree. You may be setting yourself up for disappointment if you’re only in it for the job you can get afterwards, since the work that you’d be doing in the job would at least somewhat depend on the work that needs to be done.

Re: Tips for Hiring a Data Scientist into a Tech Company

#23

Post by vidarh » Sun, Jan 12, 2020, 6:06 PM UTC

curiousgal wrote: post #13

vidarh wrote: post #3

Earlier quoted context omitted.

I agree. Last time I hired a data scientist, we got someone who could code but who'd write very much suboptimal code that worked for his exploration and as a proof of concept, but not much more. That worked out fine: we paired him with a relatively junior developer that worked with him on translating his proof of concept code into something more suitable for production. I think both of them learned quite a lot of cod…

What's a concrete example of production DS code? Does it mean that the code handles edge cases, uses logs, is well documented, etc or what exactly? I'm a DS student and don't know what everyone means by "production" code.

The other replies here, and your own suggestion all makes sense. Often it's also performance and memory use. The prototype code didn't tend to take into account which shortcuts etc. we needed to take to run things fast enough on hardware of more than a decade ago with the sheer amount of data needed in production.

Which was fine - clarify of the methods was helpful in order for the rest of us to understand what he was trying to do.

Re: Tips for Hiring a Data Scientist into a Tech Company

#24

Post by natalyarostova » Wed, Jan 15, 2020, 6:19 PM UTC

streetcat1 wrote: post #18

natalyarostova wrote: post #8

Earlier quoted context omitted.

Depends. If it’s a spam classifier, yeah I agree. If it’s estimating causal models for some type of economic analysis it will be harder for a self taught SDE to compete with someone with academic research experience dealing with messier social sciencey economic data.

Actually spam classifiers are not easy at all. First, the data is very unbalanced. Second, you would need to retrain the model often. So you would need to understand statistical methods for comparing text level/word level distributions. This is not a college-level stat. Third, you would need deep knowledge in NLP, feature engineering and algorithm selection.

I didn't say it's easy, I said it's something more amenable to an SDE self-learning.