Building credibility as a new data scientist

For many people (including me), it can tricky knowing exactly how to build credibility in data science land – especially if you’re a newer data scientist.

This topic has a lot of depth; what’s mentioned below is only a subset of potentially useful considerations.

For context, something my little group of data science industry buddies generally agree on is that it’s pretty easy to find advice on the internet about the technical aspects of data science – but harder to find thoughts (hopefully useful) about the non-technical aspects of the industry.  So here’s my little attempt…

 

False confidence is strategically a really weak way to start

This is a killer, but apparently you wouldn’t know it talking to probably 80% of MBA analytics programs.  Maybe it’s an American thing, maybe we’re at peak data science hype, but it does seem that we are reaching record numbers of data science ‘experts’ – many of whom have never actually worked in industry, let alone actually accomplished anything.

The kind of trickle-down effect here is, even if you have no idea what you’re talking about, they don’t have to know that.  Fake it till you make it bro – chest out, buzzwords ready, just spin up a cluster and you’re all set!  Your graphs will blow them away – even if they’re essentially meaningless!

The problem is, many of us have real deliverables to meet, and the last thing we need is an inexperienced data scientist coming in and apparently already knowing everything.  You can’t learn if you already have all the answers – after all, if you already know everything, what else is there to possibly learn?

Strategically, it’s way easier to be open with your team about your legit strengths and weaknesses – chances are, if you got hired, they will value you for your strengths and want to help you develop your weaker areas.  No one is perfect, but pretending you are is just that useful in data science.

 

Most ‘dumb’ questions are actually not that dumb 

One thing we’ve noticed is that, the closer your company is philosophically to an early-stage startup (vs a government job), the more ‘dumb’ questions are appreciated.

When you work for a team that is hanging out at the cutting edge and actually responsible for producing something valuable (vs maintaining bureaucracy) – no one has all the answers.

When you ask a dumb question, chances are the audience you’re asking will be appreciative of you asking the question.  Especially when we’re new to an industry or domain, the end-user or boss absolutely knows that we are not a day-zero expert – and to implicitly pretend otherwise is a strategic career killer.

Many managers, end-users, and clients have been previously burned by highly-confident (and polished) data scientists who were too confident to ask questions – who then completely botched the analysis because of a dumb little thing that could have been easily addressed if they had just asked.

Additionally, there’s a good chance that the audience would assume you have very little initial knowledge of the domain, so by you not asking dumb questions – they now have to decide whether (a) you’re already an expert (unlikely), (b) you’re too scared to ask a question (normal), or (c) you never even considered that your domain knowledge might be lacking (scary).

 

Be open about your present limitations – but have a plan for getting up to speed

By just straight up admitting that you don’t know something yet, you’re already ahead of probably 90% of new data scientists – who, for maybe the reasons above, just didn’t request the help that they actually need.

I’ve discussed this with my little group, and we all pretty much agree – especially if you’re new to the team, your manager wants you to ask questions and tell them your present limitations, tell them what you want to learn.  They can’t help you if they don’t know what you need.

However, as mentioned above, you additionally should then have a somewhat clear plan for improving.  The plan doesn’t have to be very precise; the bigger factor comes down to whether you have the inherent motivation and discipline to acquire this new skill or knowledge, in a realistic timeframe.

This topic of learning on the job and acquiring new data science skills is a much larger topic to cover later – but essentially your ability to learn and adapt is way more important than whatever your present knowledge or skillset is.

 

The views expressed on this site are my own and do not represent the views of any current or former employer or client. 

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