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How Successful Data Scientists Deal With Bad Appraisals

How Successful Data Scientists Deal With Bad Appraisals

Prajakta Hebbar
bad appraisal data scientist

bad appraisal data scientist

2018-19 turned out to be quite underwhelming for data scientists, especially in terms of monetary compensation, and it has hit many professionals hard this appraisal season. It can be demotivating to recover from a less-than-stellar appraisal, especially when it does not align with your performance review. Many professionals, especially in the thriving data science field ended up getting an unexpectedly low salary hike this year.

If you are one of the professionals who got an unsatisfactory raise, you might be feeling indignant, embarrassed or even disillusioned — especially because there has been a lot of noise about how great and thriving this industry is.

In this article, we will discuss what appropriate steps should be taken to deal with this predicament. Starting with keeping cool, and moving towards a brighter future, Analytics India Magazine will lay down the game plan:

First of all, here are the hard facts:

  1. It’s Not You, It’s The Industry: Despite being the country’s hub for the IT sector, employees in Bengaluru this year are expecting only a 0-10% increment. In fact, most of the IT bellwethers all across India, have announced an average pay hike of 6% to a majority of their employees.
  2. Not A Good Time For Middle Management: Numerous reports have suggested that this is not the best time to be a data scientist — especially an experience of more than 7 years. An experienced data scientist, as a middle-management employee, has found him/herself in a precarious position in 2018-19. Reportedly, companies who are dealing with financial crunch think that employees who hold middle-level designations like Project Manager or Project Architect, and whose projects are not yielding the desired revenues, must be asked to leave. Case in point, Cognizant is planning to hire more junior-level data scientists, while the job for many mid-level employees is in danger.

How to cope with these changes?

Self Evaluation

It may sound like a piece of rote advice, but taking a look at one’s own performance and the resulting monetary appraisal level is the place to start with the coping mechanism. Was there anything about your performance as a data scientist that you’d like to do over? If there are many such instances, then perhaps you can’t put the whole blame of the bad appraisal on the manager of the company.

Discussion With The Boss

It is always a good idea to speak to the boss about your unsatisfactory performance review. Many noted HR representatives think that there could be a possibility that a manager may have overlooked or not remembered some critical activities and initiatives that a data scientist has taken throughout the year. Reminding the manager of your coding proficiency or even critical problem-solving examples is a good idea at this point in time.

Self Goals

The demand for data scientists has increased in the Indian tech industry, but so has the supply. As a result, salary growth has moderated, especially at the middle-level, but is still one of the “sexiest” for beginners. As a data scientist takes on more and more managerial responsibilities, it becomes difficult, especially with the inflated pay packages, to match up to the salary hikes in the industry.

That is why the self-goal for a data scientist should be to become irreplaceable in the organisation. Now, this broad goal can entail taking on more responsibilities or acting as a secret sauce that keeps the entire data science team working cohesively in the organisation: Whichever the path you choose, your manager must see you as someone who cannot be replaced in the team, no matter what level of seniority you hold.


Wipro’s Sohini Mehta had earlier told AIM that it is always preferred by big companies to reskill than onboarding new people. “Hiring my sound very simple but the fact is that there is a huge scarcity of talent in the market and getting people may come at a very high price point,” she said. “Reskilling existing workforce in the company not only builds the talent pipeline but helps in improving employee stickiness to a great extent as you would have loyal people who will stay with you and work with you for longer,” she added.

Future path

While going back to work after a bad appraisal may bring you down, you have to understand that this one year will only be a small blip in your illustrious career as a data scientist. Whichever organisation you work for, always remember to:

  • Invest in new capabilities
  • Make digital transformations co-exist with traditional services
  • Re-skill with emerging technologies
  • Be open to adopting new technologies and solutions
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