Scores of Indian professionals taking up a pg in data science in India expecting that the degree will open dream salaries and high-growth opportunities. However, reality strikes back, as soon as most people graduate: all of their peers are striving to get the same position, and job websites are full of identical looking resumes. Why then do some people cut through the noise and go big when others stagnate? This is the tale that actually shifted the needle within the career of one analyst and the unforeseen actions that transformed a small 6 LPA position into a 28 LPA offer in less than two years.

A PG in Data Science in India Is not Your Ultimate Weapon

Do not get me wrong, a credential is capable of opening doors. It is what you do when you get it which gains leverage. Ankit is one of these professionals who learned it with his skin. After a pg in data science in India, Ankit was ready to get called by a lot of recruiters. In place of that he found silence.

This is what came to change: he no longer waited around and instead he began to start treating his career like a product launch. All his projects done, either being freelance work, Kaggle competitions, or open-source ones were presented in a compelling packaging. He stopped being a person who learnt Python and turned into the one who solved real forecasting problems party of a D2C brand using Prophet and ARIMA.

The degree assisted him in getting the language of data science. However, he stepped on the real career acceleration after learning to talk the language of value.

Strategic positioning > Blind Upskilling

A majority of the Indian professionals are trapped in the more skills-more offers syndrome. Ankit didn't. In the short time after landing his first job post-PG at a small analytics company, one major fact struck him like a ton of bricks: his company was selling bundled analytics services to internationally based clients, yet the majority of juniors in analytics firms were doing the opposite of meeting customers. He never turned down a client call even where it involved overtime.

Why? Visibility.

By answering calls, drafting post-analysis reports, and translating model results into non-technical terms to explain them to the stakeholders, he earned credibility, which is a quality not attainable in a classroom. This was what made him not only a data scientist, but a communicator between data and business outputs. The next interview that he went in to take up a position in, he led with stories, not stats. Employers adore demonstrations of influence, not talent.

The Secret Personal Branding

As soon as Ankit crossed the 12-15 LPA mark, he began to use LinkedIn as his second resume. Not to take selfies or humblebragging, but to post brief analyses of projects (redacted to be confidential), insights of tools he has tried, and what he has learnt in bumpy situations during a deployment.

He moved into the limelight. A single discussion about the reason why most Indian data science teams are misusing A/B testing resulted in a chill response of a hiring manager of a fintech company. After six weeks, he received a 28 lpa offer (not because of the PG in him, but because someone found him as solving a problem, rather than being a word match).

Take Your Next Step by following the Market

The other lesson: degrees do not attract reward in the marketplace, scarcity does. Ankit did not make his resume hot by picking up every hot tool he could, but specialized in at least 2-3 areas with limited supply and high demand, i.e. time-series modeling, causal inference, and basic ML ops. These were not the most glamorous capabilities, but they were unrepresented among his competition in the peer group, but they perfectly matched the companies he wanted to target.

Conclusion

Provided that you anticipate a pg in data science in India coming to your rescue and do all the hard work, you are bound to stall. However, in case you view it as a launch pad, which allows you to accumulate the necessary amount of knowledge to create, the required amount of credibility to present, and the needed amount of self-confidence to become a tester, then it can be a game changer.

The jump between 6 LPA and 28 LPA did not have anything to do with luck and privilege. It was leverage, story and fearless implementation. Today, the professionals who succeed in the ever-changing data environment are those that not only learn but they also construct, disseminate and tactically appear.