Over the last 16 years, while building the Data Science Centre of Excellence (COE) from scratch for multiple organisations, I have come across many Analysts, Data Scientists, Data Engineers (add as many titles you know here). Some of them created strong impressions in my life and I would feel proud to have them as my colleagues in whichever organisation I am at, or to recommend them to other organisations where I know they would get a good growth path.

At the same time, what surprised me, when I saw the same person, in one organisation did so well, but in another organisation wasn’t adding enough value even though they are capable of. This made me think, what makes one data scientist rise up to a Great data scientist?

I spoke to almost 300 of them (Junior/ mid senior to senior) and observed their progress minutely, and the only word that remained common among them was ‘culture’. Much research has been done on how culture improves productivity, so, I won’t go down that generic path. It’s as easy to say the word ‘culture’ but it’s equally difficult to define it. I have found the largest of them – at least the one that matters to Data Scientists – and this can be applicable to Sr leaders who are trying to build a great data science team and for individuals, who aspire to stand out as a Great Data Scientist.

To learn how to become a Great data Scientist, read the blog here.

Knowing WHY you work drives HOW you work, which in turn makes you stand out

At times projects come to you in a very abstract format (where you are not sure how to break it down) or as multiple specific tasks.

For example: Someone asks –

(a)   Run a consumer segmentation model and show what different persona of consumers are using our product/service. – This is called the ‘Abstract’ problem.

(b)  Number of consumers coming daily on our platform vs once a week vs once a month. What % of them order from us and what’s per consumer order value? – This is called a ‘Specific’ problem.

 In either of the cases you have 2 options:

1. A)    If it’s specific, then do the analysis and share the data as asked. If it’s ‘Abstract’ then do a first cut as per your understanding and share. If business wants something else they will anyway get back.
2. B)    Speak to the person who has asked for them, and ask ‘The Context’ behind these analysis.

If you follow (A) you are a data scientist, but if you do (B) you are the one who stands out.

Here is why:

When you ask the context – you learn about the larger business problem. You learn why they are asking for this data. Once you learn the background, you can think better, and you would be able to bring bigger value than just delivering what they have asked. 

Let’s take the case of (b). When you ask them, they say “well, you know we have to improve our quarterly revenue by x%, and one of the ways to do that is by converting as many users coming to our platform to purchase, and those who are regular on our platform, we are trying to improve their cart value”.

Now when you know this context, you will not just share the numbers/ information they asked for. You will go the extra mile and do a proper RFM segmentation. You would profile those segments with the kind of products each of these segments buy, so that business can target them with similar or complementary products. As a next step you can even do a ‘Next Best product to buy’ model, where for every consumer/ micro segment you tag a set of 3 products they are highly probable to buy. Your analysis adds way higher value for business to take an action, and Your value, in turn, to the business team grows by 3-5X. Because you move from a ‘doer’ to a ‘thought partner’. 

Could all of us data science practitioners take a pledge to ask 'WHY', before jumping to any analyses and elevate ourselves to 'thought partner' level?

Share your experience with us at mitra@setuschool.com