Should You Start Your Data Science Career With Big Tech Companies Or Startups?
Getting started with Analytics or Data Science career often leads to the crucial question — should you be looking for a job in a big organisation, or should you experiment with a vibrant startup? Both startups and big firms are on a constant lookout for candidates for their data science teams and a candidate may often end up being in a lurch when it comes to picking one out of the two. Some of the bigger firms that hire fresher for analyst position straight from college and campus placements are Mu Sigma, Fractal Analytics, WNS, Absolutdata, ZS Associates, VISA and EXL Services, among others. Whereas there is a slew of startups such as Tredence, G Square Solutions, Spoonshot and others that are on a constant lookout for data science skills too. While both startups and large firms provide learning opportunities, there are many differences when it comes to job roles, culture, pay and perks, among others. If you have been offered a job from both startup and big firm, the big question is which one should you opt for? While it is definitely easy for bigger names to attract talent, both startups and big companies have their own advantages and disadvantages. Getting your hands dirty with data Exposure to data is one of the major considerations. It goes without saying that larger firms have a big team of people who work simultaneously on solving problems, populating the data resources that they have. Whereas startups provide a larger opportunity to get your hands dirty with data as you would be responsible for working on a problem from the scratch till attaining some results. Having said that, the data resources may vary significantly in larger companies and startups where prior may have more sorted data structures, unlike startups where it can be noisy and unstructured. Opportunity to develop new skills One of the negative points for larger companies is that data scientist may get stuck working on the same problem for years. They may be working on the same tools and with the same people, exposing them to lesser growth opportunities and lesser exposure to newer tools. On the other hand, startups have an undefined path and they get to work on different problem statements quite frequently exposing them to the better learning path. Underutilisation of skills Larger companies may fail to utilise all the skills that candidate might have attained during the course of education or training. For instance, a candidate may be stuck in programming a particular language without getting a chance to utilise any other tool at all. Whereas in startups there are chances that they do not have a lot of processes already built in place, exposing them to larger utilisation of data science skillsets. Core vision and learning opportunity The core idea of a startup is to get the most of data-driven decision making to drive insights and start making profits as early as possible. The idea in larger firms may differ from building products from scratch to improvising on current products giving less exposure to new skills and processes. In big companies, it is more of following a set target, guidelines and deadlines, unlike startups which allow for learn-do-and-deliver. Multitasking In startups, the range of work may vary from coding to marketing and sales at some point exposing to new opportunities and expanding horizons. It isn’t uncommon for a data scientist at a startup to be juggling five different problems at the same time. Larger firms may have the opportunity to work for months and years in the same problem, allowing a lesser opportunity for multitasking. Support system One of the bigger advantages of larger firms is that you may not be the only data scientist working in the company, unlike most startups. Startups are all about defining your own goals and keep motivation factor high. Whereas in bigger firms there are more experienced data scientists who can answer your doubts. The need to perform better With a smaller workforce in startups, it is highly important for startups to keep performing better to contribute for the better success of the company. Whereas in bigger companies where there are more people to rely on, one person’s performance won’t necessarily push you to perform better. A startup may make data scientists do menial jobs While the startup is a lot of learning, it may ask people to get into performing menial tasks such as data warehousing and data cleaning. But the flip side is it gives a lot of freedom to explore data sets, also allowing to work on her own data projects. Who Wins? There are several other factors such as stability, pay and perks, security and training opportunities that come into the picture. As discussed above, working in a startup and big firms have their own pros and cons. They both have risks and it is important to understand the risks to be able to better manage them. While a startup may provide a steep learning curve it has cons such as not being exposed to proper training. On the other hand, big firms have limited problems to work on as opposed to startups which is a place full of opportunities. The best way to sort out on picking either is to have a clear conversation on the work profile, the kind of problem statement you would be handling, tools you’d be using, growth opportunities and more. The choice can ultimately be made based on an individual’s likings and interest.
Getting started with Analytics or Data Science career often leads to the crucial question — should you be looking for a job in a big organisation, or should you experiment with a vibrant startup? Both startups and big firms are on a constant lookout for candidates for their data science teams and a candidate may […]
While it is definitely easy for bigger names to attract talent, both startups and big companies have their own advantages and disadvantages.
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["AI", "data science", "analytics", "R", "Go", "GAN", "data-driven", "startup", "programming_languages:R", "programming_languages:Go"]
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.