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My Journey To Getting A Data Science Job As A Fresher — Part 2: The Hustle


My Journey To Getting A Data Science Job As A Fresher — Part 2: The Hustle


In my last article, I wrote about how I began my journey in Data Science. You can read the first part of my story here: My Journey To Getting A Data Science Job As A Fresher — Part 1: The Struggle



So to pick from where I left last time, I managed to get an interview opportunity with a company named Squad for an analyst internship role. I did well in their phone interview and take-home assignment round. But was rejected in the last round, as the role was a bit non-technical, required more of excel expertise than Python knowledge. Still, I felt I was lacking somewhere foundationally. 

Note: I want to make one point here — I seriously don't like the concept of take-home assignments. It works for the employers but it takes a hell lot of valuable time of the candidate!

After some introspection, I took a free membership of DataCamp. I made up my mind to brush up my basic Statistics skills. From probability distributions to densities to hypothesis testing, I was getting good at everything. I took KhanAcademy's course to completely understand the ins-and-outs of Hypothesis Testing. By now, I had gained a lot of confidence and it was the time I actively started looking out for companies seeking freshers for data science job roles. 

“Companies Seeking Freshers”? Who Hires A Fresher For A Data Science Role?!

If it was a year ago, I couldn't just make it through, but this time as I said I had become foundationally strong, I knew what to solve and how to solve. I started getting “real” interviews. I was now very particular about the products of the company I was applying for. I wanted to get hired by a company with my full satisfaction. 

Money was not on my mind, but satisfaction was. But sometimes money brings satisfaction too. 

I applied to a holiday-homes Indian startup. I got a chance to get interviewed there. I met the HR, I was given a bunch of Python questions on HackerRank platform, with Fibonacci Sequence being the toughest amongst them. 

I successfully completed all of those and then met another guy who took my ML interview. It went well in starting, I was talking about my learning in the field of statistics. A bunch of ML questions then. I could answer some and some I couldn't, but there was some negativity I felt from the interviewer's side, he didn't sound professional which made me somewhat less interested in this job. 

I had the other interview with the founder. He was a polite person, he asked me some behavioural question to judge if I was a good fit in their culture. I answered all his questions pretty honestly. He was amused by the fact that I was about to leave my highly-secure, full-time job at Fidelity for a startup. I didn't know it mattered too much for him, but I was just being honest all the times. 

The HR later told me that I was selected and discussed the compensation details with me. I went home telling him that I'll let him know about my final decision the next day. The thing was, some people there didn't fit well with my work culture. I found negativity in interviewing with some. That’s why I made the decision to let go of that offer. 

I gave more and more interviews. At one time I got an offer to Head the Technology of a very early-age startup. The guy was amazing, had an amazing idea, but I was not in a position to Head the tech, which had the initial focus in managing all the development-related work. I wanted to gain experience in the data domain. We became good connections and I moved on. 

The Day

Finally, I got matched with a US-Canada based real-estate startupI talked to their CTO for a data science opportunity. The very first thing we discussed was my interest and things I was passionate about. He was interested in hearing my journey and was delighted to know how unique my path was for not settling for a safer job at Fidelity, but looking for a role I was passionate about. 

We met for an onsite, at a lovely coworking place. I was given a coding problem to solve. I did it quite fast, wasn't tough, it was just a tick in the box to know if I can code neatly. We then discussed my projects, a small data knowledge round was conducted. We discussed how we can solve problems at their company using data. I came up with a few solutions, and I was getting more and more comfortable with such a non-traditional interview experience. It was very different than what I had before. 

After that went through a stress-test round. I was asked some very straightforward questions to which I was only able to answer with my full truthfulness. Some of the questions were as follows: 

  1. What do you do when you can't solve a problem?
  2. How do you relieve stress?
  3. Was there a thing which you found impossible to solve, what did you do then?
  4. What drives you, why are you here, why not at your former company?
  5. Some algorithmic puzzle question. Just to check how well I think under pressure.

I had experience-rich answers to all of these. I did let him know my true purpose. He was impressed with how I was driven by purpose. I did ask for feedback from him and he happily did so. 

 And finally, I was hired.

I really feel that answers to such assessments must be at the utmost importance for any employer to look for in a candidate. We finally discussed the compensation package and I loved the way of how considerate they were for their employees. They offered me a package, taking care of my travel, my parents' worries, and my satisfaction. 

See Also

My checklist at this company was like:

  1. A data science role. Check.
  2. Awesome team. Check. 
  3. Working in a startup. Check.
  4. Working out of a fabulous co-working space in Gurugram. Check.
  5. Worries. Unchecked. 

It was a very satisfactory offer, and I accepted.

Very few people are fortunate to get lovely, enriching interview experiences. I cherish all my interviews where there was a lot of positive vibes be it the place where I got rejected. You have to give 2/3rd of your day at the workplace, make sure you will like it, you will like the people, the culture. A cultural fit is a two-way thing. Make sure your team fits with your cultural zone as well. 

Retracing The Steps

At the beginning of this post, I promised that I'll show you a good straight-forward path you can follow to land your first data science job easily. You have seen I have messed up the order, which is the number one reason I took so much of time. I want to make it easier for you and save you from getting into unnecessary and not-so-useful stuff.

 I can never spell 'unnecessary' correctly. Can you?

  1. A statistics background is a must. I will first encourage you to join DataCamp. You can join it for free until two months through Microsoft Visual Studio Benefits. I don't use DataCamp much for other purposes, but I would recommend you to take the "Statistical Inference 1 and 2" from the data analyst track. (Give it a week's time)
  2. Take Khan Academy's Probability and Statistics course. I feel, doing only the Hypothesis Testing part from the playlist will suffice and will make you foundationally strong with Hypothesis Testing. (A week's time is enough for this too)
  3. Pick up the 100-Page-ML-Book. I usually read fewer books. But this is a keeper. The book is available for free here. It is based on read-first-buy-later policy. I bought this, it was worth it. You will get an amazing overview of much of the Machine Learning field with actually not ignoring the mathematical stuff. This book is endorsed by Peter Norvig as well. (Read it along while learning)
  4. Try implementing some ML algorithms on your own. Or take the Andrew Ng's Stanford ML course. You need not do it end to end. Do it topic wise. (Give it 3-4 weeks)
  5. Let's say you want to get in conversation with today's AI Scientists. Take up a Deep Learning course. I found the Udacity's free "Introduction to Deep Learning course with PyTorch" much more intuitive than the other too overwhelming ones. Andrew Ng's course is still one of the best, but I would suggest you take it when you want to go into much more detail, or when you actually are trying to solve a real-world problem and can refer these materials to know the algorithms in and out. (Give it a month's time)
  6. By now you'll be able to read research papers. As a fresher, implement some research papers you like and add it to your portfolio citing them. It will make your employers able to consider you for some position.
  7. Pick up personal projects, jump into Kaggle. This is the time you'll learn along the way.
  8. Do not stop learning. 

Whenever you feel confident enough, start applying for jobs.

Pointer For Interviews

When giving an interview remember the person interviewing you doesn't know you at all. They don't know how hard you have worked for it. You need to tell your story. You need to make them realize that this isn't just another job for you. You are here because you are passionate. You are driven by your passion. Tell them that you don't have the experience, you have the will. 

Be honest, do not lie. Your employer would love to have an energetic, passionate human being who is driven by what he or she does.


Also Read: How To Become A Data Scientist In 2019: A Beginner’s Guide



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