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How A Data Scientist Thinks.

How A Data Scientist Thinks.


A walk with our Chief Data Scientist Dr. Nilesh Karnik is always a delight. Add to that the pleasant monsoon mornings, in the beautiful surroundings of Powai and you have a winner. We were both early to office for a client meeting. When we were 3% of the distance (we are an analytics company, nothing comes with approximation!) through to the client’s office, they called and requested us to postpone the meeting. In a world where customers are Gods, we were left with a not so attractive option of turning back on a water-logged-traffic-jammed road towards the safe haven of our office. Having a bit of unplanned time on hand we decided to take a break at Le Pain Quotidien (LPQ) at Central Avenue Powai.

We sat in and ordered the delightful coffee at LPQ. Our conversation jumped from novelty in the method of serving the coffee to novelties in chess openings to rules of Bridge to ultimately my favourite casino game – Black Jack!

You should be able to connect the dots!

This is where I got a chance to pry into the ‘HOW of things’ with respect to Data Science. I have been searching and recruiting data scientists for close 2 years now with some help from LinkedIn, blogs and some excellent content from O’Reilly seminars; so I am pretty much there with respect to the WHATs.

Since we were discussing Black Jack, Nilesh pensively pointed out Ed Thorp and his book ‘Beat the Dealer’. If you take a closer look at the story, there are two rival sets of Data Science experts. Edward Thorp is one set.  The second set is the Casino “Analytics” team which marked him out, and changed the rules to counter his strategy. I am ordering a copy of “Beat the Dealer”; not to win Black Jack but to understand the science.

While Ed Thorp figured out a way to beat the dealer using data, the casino guys figured out a way to create rules which will eradicate the strategy of Ed Thorp. In round 1 I am sure Ed used data from experimentations done outside the Casino to devise the method and perfected the method using trial and error. The casino guys tried banning him and then used instances of his games to change the rules. The rules were changed without making them unattractive for the other players. In either case, the auctioning side used data and analytics. Both sides used augmentation and ran trial and errors before coming to actionable conclusions.

On the walk back, Nilesh asked me if I was in positive or negative with respect to the overall Black Jack experience across the globe. I told him Dutch casinos have been unkind as they take your money on a tie; but overall I am in the positive because of a single large win at an Australian casino. And there I got another bit of history into Data Science – ‘The Black Swan Theory’   by Naseem Nicholas Taleb. The context being that the black swan event averaged out my winnings on Black Jack table to green; however subtly highlighting that it was an unpredictable event because history of my losses till then would never predict a big win using mathematical predictive techniques. If you look at it paradoxically from the perspective of the Casinos, Ed finding a method to beat them continuously was a black Swan event. They analyzed instances to prevent this from happening in the future.

Within the above examples Nilesh had highlighted to me what goes on in his mind every time I bring a problem to him. Why certain assumptions he is not willing to accept and why his questions on certain less frequent events are so rigorous. The following can give a general understanding to non-practitioners when interacting with practitioners of Data Science.

  • Rule 1: Know thy data
  • Rule 2: Be open to what you can augment & be selective with respect to what you augment into your data and models
  • Rule 3: Data speaks; give it a canvas of methods to communicate to you
  • Rule 4: Do not be prejudiced
  • Rule 5: Build models to exploit positive black swans and restrict impact of negative ones
  • Rule 6: When cleansing data for use do not introduce your influence into the data. Else the data will tell you the story that you want to hear.

Well I am sure there are many more. These also define the tough job Nilesh and team do every day -“Define products which use data science & big data to solve business problems”.

And for that cuppa!! I can safely say LPQ will be seeing more of me and Nilesh at their outlet.


About Nitin Purohit

With over 15 years of experience in leveraging technology to drive and achieve top-line and bottom-line numbers, Nitin has helped global organizations optimize value from their significant IT investments. Over the years, Nitin has been responsible for the creation of many product IPs. Prior to this role at Aureus, Nitin was the Global Practice Head for Application Services at Omnitech Infosolutions Ltd. In this role, Nitin was responsible for sales and profitability of offerings from Application Services across geographies.

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