A Ridiculously Specific Guide on How to Get a Job in Data Science

. 4 min read

At GreyAtom, after transforming and pivoting so many careers, we have a specific recipe for success that is data-driven. We analyzed the data of all our learners: those who got jobs as Data Scientists after doing our program, and those who didn’t. A lot of patterns emerged.

Stay positive

The reality is that every time a company doesn’t make you an offer, it will hurt. Even though you were going to turn them down, it will still feel like a punch to the stomach. You’ll question yourself: are you smart enough, young enough, experienced enough, prepared enough? You'll ponder about how to get a job in Data Science. This is normal. It sucks. Embrace the disappointment. Vent to your friends or have a tall glass of Scotch. Whatever your process, work through it, all the while repeating in your mind: “I am enough.” Because you are. Then loop back to step one and give it another go.

At GreyAtom, a specialized career team will work with you, as you go through this important phase of switching careers and taking the leap. We will skydive with you — in tandem!

Deep dive into the data

Hold on, we about to go meta. We used data science to find insights in placements. We asked the data a few questions, and it delivered answers. Data science!

1. Which experience levels have got jobs?

80% of learners were able to transition their careers. In fact, learners with 0-5 years of experience fared the best. Good news for freshers and those without a lot of industry experience.

2. Which companies have hired learners?

Big names on the graph. GreyAtom also hired a fair few of our learners, because they were THAT good.

Note: For the sake of simplicity, companies that hired single candidates are not included in this graph. These companies include Fractal Analytics, EY, Deloitte, Reliance Jio and PwC, among others.

3. Percentile performance in our program

There was a definite correlation between the people who got jobs and how much they scored in the program. Those with at least 75% did exceptionally well.

4. Has the student completed hackathons and their capstone project?

Placed students

The data is fairly self-explanatory here: learners who attempted more than one hackathon and submitted a capstone fared better.

5. Average attendance in the program

Learners with at least 85% attendance saw the best results.

6. Average number of jobs applied before getting placed

The golden number for applications was 8. It is key not to be disheartened and keep trying.

Step-by-step approach

Our team of career coaches will work with you across three key areas:

  • Career clarity and goals
  • Career connects
  • Career competitiveness

Which can be further broken down into steps, as shown in the chart below.

Help us help you

You, as a learner, need to meet us halfway, and fulfil responsibilities of your own.

  • Attend at least 85 % of classroom sessions
  • Submit your capstone project
  • Participate in hackathons
  • Build a project portfolio - at least 2 projects in areas of your interest on your Github profile
  • Attend Career Services workshops and sessions, and implement learnings on your social profiles and resumes
  • Attend industry sessions
  • Keep your career coach updated about your career search journey

A last word

We are committed to guide and facilitate the job search journey for upto 6 months post program completion.

Generally speaking, how quickly a learner lands a job largely depends on how many jobs he/she applies to per day. Our recommendation is to apply to a minimum of ten jobs per week. Club that with all our pointers above, and soon you'll be able to advise other people on how to get a job in Data Science.

Our current outcome percentage is 88%. This statistic refers to all the learners who were looking for a transition, and were able to achieve that goal. We are striving hard to take this number higher.

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