A Day in the Life of a Data Scientist
By Aditi Bhat
Me Every Morning
It’s 6 am. This is my time. My mornings start with a mix of reading, puzzle-solving and exercise. I dedicate these three hours just to myself, away from the phone - and the world connected to it.
Fun fact: According to a study, morning people are less likely to procrastinate.
I start with a jog to the club where I do some form of exercise. Today, I chose to swim. After the swim, my hunger pangs kicked in a little earlier than usual. While eating breakfast, I continued reading Freakonomics – an interesting take of a rogue economist on the hidden side of everything. The book heavily uses data to identify and study some common and not-so-common phenomena. This helps someone like me gain a fresh perspective on things. Later, during my commute to work, I solve my daily puzzle. Currently, I’m obsessed with Japanese puzzles!
Also, did you know that the average speed of a vehicle in Bangalore traffic is just 9.2 kmph? That gives me a LOT of me time!
At 10:15 every day, we have our ‘Position-Progress-Pain’ meeting, it’s basically a team status meeting to share our current status, progress and pain points on the project. During this meeting, we spend some time discussing the different ways we could analyze the same data sets to predict different outcomes. We study various scientific papers that can help us understand and eventually solve the problem at hand or even discuss new tools that we could use in our field of work.
The stat that led us to a 14-minute only meeting: executives spend up to 50% of their working hours in meetings. Up to 50% of that meeting time is unproductive!
Time to Work*
After the meeting, with a goal in mind, I’m in my zone and ready to tackle the task at hand.
During this time, I’m constantly looking at data and shaping/modeling it. I ask myself the questions that I am going to answer by the end of the day. In the field of Data Science, one has to be curious, that is because to find the answer to one BIG question – a Data Scientist needs to ask - and answer - several other relatively smaller questions.
After I’m done getting my ‘small’ answers, I need to translate my work into something that will be useful to my stakeholders. I use the word ‘translate’ because our complex data models are far from easy to understand.
Quick fun fact: More than 90% of all the data in the globe was generated over the course of past two years.
In Between My Work
Lunch is sometimes eaten while helping my colleagues write code to calculate a Gaussian Hyper-Geometric Function or model different probability distributions. Lunch is never just eating.
Wrapping It Up
This is when I give attention to all the things that don’t need my immediate attention. Non-urgent Emails, quick client calls and everyday managerial tasks take up this part of the day. I am currently sitting with the team and looking at their arching patterns that they have created with the help of data modeling tools. This is the time when we step back and check if we’re getting any closer to answering the BIG question.
It’s about 8:30 pm (on an average) when I get home. But the night is young. I get my dinner and crawl in bed watching a sci-fi movie/show or continue reading my current book. I’m currently watching this show called ‘Mr. Robot’ and I am absolutely loving it.
Last fun fact: Netflix users watched 42.5 billion streaming hours in 2015 alone!
Plot Twist: By the way, I’m just a BOT taking a shot at how a day in the life of my boss looks like. Let me know what you think about it from a human perspective! I’ll take your data into consideration and try and improvise. Thank you.