4 Secrets for a Future Ready Career in Data Science

Tavish Srivastava 19 Nov, 2018
7 min read


Automation has impacted, and will continue impacting, jobs in many domains. Every single job on this planet is subject to a risk of job replacement by bots – just the intensity might differ. Automation makes running a business more efficient on one hand, and on the other, it keeps on changing the skill set required to stay relevant in the industry.

This inevitably leads to unemployment due to mismatches in the skill set. Let me take you through a few scenarios to illustrate my thoughts.


Scenario 1 – Manual Roles

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You are an HR professional in the year 2000, when most of the company employee documents were on paper. You are very efficient in sorting documents and retrieving them when needed and have been a star performer for more than 5 years because of these skills.

Given that the HR processes did not change much over time, you did not pick up computer skills over the next 18 years. However, the way industries work have changed a lot from 2000 to 2018, and now all the employee documentations are on the cloud or a private server.

So, your most sell-able skills are now suddenly not that important. You might face difficulties finding a job unless you upgrade yourself for today’s evolved industry. Note that your skill set mismatch was not because of the evolution of HR specific processes, but the dynamically changing business processes that you support.


Scenario 2 – The Preference of Customers

You work as a news reader on the radio in the era when there was no television. You are very well informed about current affairs and hence you were a strong performer. But after television became mainstream, radios almost went out of business. Your radio employer had to let you go because they were sustaining heavy losses.

Now, given your skill set, you can still try to get a job as a TV news reader but you need to work on your body language and the crippling fear of facing the camera. The good news? You have been hanging out with people who work in the TV news industry and hence you know your opportunity areas and have been actively working on them.

Note that this time neither your profession evolved, nor your industry. It’s just that the customer started preferring an alternate product/service to the business you support, making your skill a mismatch (or obsolete) in the industry.


What did we Learn from these Scenarios?

In the scenarios above, we witnessed that the changes around us are making businesses easy to run but at the same time are  creating job skill mismatches, leading to unemployment in specific domains. Below are the three main reasons of job skill shifts in the industry:

  1. Tools and technology used in your profession are changing
  2. Business style that you are supporting is changing
  3. Customer preference for the product/services you support is changing

It is no surprise that automation and changing business domains have disrupted many jobs. An important questions now is:

Will some jobs be impacted more than others?

Even though no one really knows what jobs will be more/less impacted by automation, here is a framework that helps understand the broad idea. Machines are not good at learning from too few examples and machines are not good at being creative. So if your job has these two attributes, you should be just fine. For instance, driving a car is a very repetitive process and does not involve a lot of creativity. Hence, cab drivers are at a high risk of their job facing automation.


Is Data Scientist a Robot-Safe Profession?

In this ever-evolving AI-led world, we (data scientists) are definitely on the better side of the deal. Where does the role of a data scientist fall in the graph shown above? As a data scientist, we do a varied set of jobs to help businesses grow. Each of these jobs fall at a different place in the graph above. The below image shows my thoughts on the different sub-jobs we do as data scientists (proportion might vary with individual roles):

As you can see,

Not all the parts of a data scientist’s job come with a 10 year warranty. Depending on your specific role and proportion of work that is difficult to automate, you can estimate your risk of automation.

Consider a data scientist in 2010. Key skill sets were knowing logistic and linear regression, and conversant with base SAS and MS Excel. Now, if we bring this data scientist of 2018 without any significant upgrades on tools and technique, he/she can face hard time finding data scientist job.  With good certainty it can be said that even though the data science stream will stay up and running for long term, the roles and responsibilities of these jobs are up for big changes. People who have challenges upgrading to these new roles and responsibilities will face strong setbacks in progressing in career.

Given the young workforce in data science field, skill set match is not a concern over short term as most of the people working in this field have recently picked up knowledge in latest tools and technique. However, as the field gets old so does the workforce and skill set mismatch within data science domain is definitely possible if this workforce is not able to upgrade their skill set while managing their daily jobs.


What can we do to make sure we stay productive and irreplaceable in the long run within the field of data science?

Four things I would recommend for data scientists in any kind of role to build a future proof profile:

  1. Master the latest and greatest tools and technology available
  2. Understand the changing landscape of the business/domain you work for and how this change will impact your work
  3. Always think about the incremental business value your work creates
  4. Stay updated on the domains beyond your current domain

With a high focus on data-driven strategies across domains, data scientists are kept busy with their job at hand. Not staying updated on each of the 4 pointers mentioned above can be dangerous in the long run.

To fill this gap in the industry, Analytics Vidhya has handcrafted a four day conference – DataHack Summit 2018. After the success of the Summit last year, we have further optimized the schedule to pack it with everything you need to know to come up to speed in terms of tools, technologies, and business domains.

Sounds too good an opportunity to pass up? Good! Tickets are almost sold on, so grab yours here TODAY!


How can you best use DataHack Summit 2018 to upgrade your knowledge on all the 4 key pointers?

  1. Master the latest and greatest tools and technology available – The best way to get acquainted and master new technologies is by using them. Hack Sessions and workshops are specifically geared for this purpose. Depending on your current skill set, you can choose from among 8+ workshops. Here is a link to all the workshops. Let’s review the most interesting workshops according to the different audience segments:
    1. I am a newbie in data science – Deep Learning and Machine Learning are the new buzzwords so you should know what they really are. Three workshops I definitely recommend are
    2. I have been in a data science role for 2+ years and have built deep learning models before – You might want to upgrade your skills to the latest and greatest tools available. You might have already used deep learning libraries like Tensorflow and Keras. Two things you definitely want to upgrade – imperative programming libraries for Deep Learning like Pytorch and Machine Learning for Big Data. Two workshops which are specifically for these two topics that I recommend:
    3. I am already an advanced user of Machine Learning and Deep Learning libraries – My top two picks for you will be:
  2. Understand the changing landscape of business/domain you work for and how this change impacts your work – This is one of the most underestimated skills because we generally believe that all we need to know is data science related updates. However, the changing landscape of business is as important as that of data science related tools and techniques. For instance, the penetration of online stores has increased significantly in the last decade. The behavior of the customers shopping online is quite different from that of offline shoppers. This difference should always be noted while creating any strategy for business. With 60+ power speakers and 15+ hack sessions, you will be able to understand the latest and greatest technology changes and business framework shifts across industries.
  3. Always think about the incremental business value your work creates – Interacting with 200+ thought leaders will help you understand the value creation through data science across industries. These new tools will enable you to better assess and prioritize your own projects/work streams based on the value created for your business in both the short term and the long term.
  4. Stay updated on the domains beyond your current domain – Interacting with employees of 400+ organizations in 50+ sessions will help expand your knowledge beyond your current domain. This knowledge is critical for your growth as the maturity level of analytics being used in different domain varies significantly.


End Notes

Here are a few helpful links for DataHack Summit 2018:

We were overwhelmed by the response we got from the participants at DataHack Summit 2017. Let’s make DataHack Summit 2018 an even bigger success!

Tavish Srivastava 19 Nov, 2018

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers


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android application 19 Dec, 2018

Data scientists are a mix of mathematicians, trend-spotters, and computer scientists. The data scientist’s role is to decipher large volumes of data and carry out further analysis to find trends in the data and gain a deeper insight into what it all means.