I don’t blame you for believing that a great career is built in a big, Fortune 500 company. After all, it is years and years of social conditioning that draws us to the big shiny object.
I also have to admit; the rise of the startup culture is also a somewhat recent phenomenon. “But I’m a Data Scientist. Everyone wants me. Why should I work for a startup?”, you’ll ask. Here’s the answer.
While the glamor of a Fortune 500 company or a large organisation is hard to resist, the fact is there are more exciting data scientist roles out there, where skills matter more than the regular credentials. If you can look beyond the glamor of the big brand, here’s what you’ll find if you join a product startup as a data scientist. Now, this comes from my years of experience, which I do accept is not a statistically significant sample size.
But first, let’s understand what a data scientist is expected to do.
The role of the data scientist, generally speaking, is that of a facilitator who can extract meaning and interpret data. This requires the knowledge and application of statistical modeling and machine learning algorithms and the capability to apply these to answer pressing business questions.
The data scientist is somewhat a jack of all trades – an analyst, mathematician, trend-spotter, a software engineer, a business communicator, a data miner, troubleshooter…basically a key stakeholder in any data-driven enterprise. And today, all forward-thinking and successful companies are data-driven.
Now, why do I say that joining a data science product startup is a great career move? Here is a simple laundry list.
Are you a generalist or a specialist?
What are your objectives behind choosing this career path? What were your motivations? That the data scientist’s job has been touted as the sexiest job of this century is a valid starting point. But what next? Life in a big company for a data scientist could mean working on the same problem for years. That’s an opportunity for sure. Why wouldn’t you want to work at Facebook to develop the world’s greatest recommendation engine? But what if you were to work on the same problem for an indefinite period of time? You could be spending your entire career in a regulated specific field.
In a startup, on the other hand, you will be putting ‘all’ your data science skills to work. Given the dynamic environment of a startup, you will spend time helping marketing, sales, product teams and the rest answer their questions. Your ability to juggle different problems at the same time and the ability to acquire and apply new skills will be your expertise. And that, fortunately, is a great skill to have. New tools and technologies will be your playing filed. You will be a specialist and yet a generalist. Obviously, your knowledge, over a period of time will only be more.
Grabbing the low-hanging fruit
In a big company, you get assigned a task and you stick to it. You have a problem; you go to the expert and get a solution. Great! But how does that impact your growth? Growth happens, in this environment, when growth does.
In a startup environment, a data scientist has the opportunity to grab the low hanging fruit. There’s data everywhere. In fact, all companies are on a mission to collect as much data as possible. Data Scientists in a startup can completely leverage this data frenzy, find unique data sets that no one has looked at before, and identify more avenues for creating and exploring more data sets. The result? You identify more opportunity.
The impact opportunity
As a corollary to the above point, making an impact is substantially easier in a startup. Of course, you have to shoulder the burden of accountability but, (as Spiderman says), with great power comes great responsibility, isn’t it?
As you get the opportunity to explore more data and discover more relevant data sets, you also get the opportunity to create high-impact prototypes quickly. In a large organisation, on the contrary, you might spend more time only maintaining such prototypes. Or you might end up working to marginally improve the performance of the existing systems. Where’s the impact?
You’ll be the ‘Data Person’
While you might be called a data scientist, you will actually be the ‘data person’ in a startup. You will help the organisation make sense of the data they have, build data systems, and identify ways to run experiments and grow the business in a data-driven manner.
Practically speaking, you will be helping the startup making sense of the data, identify ways of how this data can help the company succeed today. How do you do this? By providing smart answers that lead to smart decisions and propels the startup to that hockey stick growth.
And then as the startup grows, you also get the opportunity to help the company become a data-driven organisation for tomorrow.
Your personal growth
‘Growth’ in the truest and the broadest sense happens when you are working in a startup environment. In a product startup, a data scientist gets to explore not just the data, but more. Think domain, customer experience, business impact, trends, and technologies.
The depth of the work is such that it helps you explore more and do more. Your role can be a force multiplier of how to help the company now. And the wrangling involved to achieve this brings great learning and consequently great growth. It no longer remains about using Spark or RNN’s to work but to explore how well these and other such technologies can be used from a business and cultural standpoint.
There’s a ton of learning and education involved when working in data science for a product startup. And while the environment will be challenging, the opportunities will be more, the impact will be resounding, and your own personal development will be phenomenal. Have I convinced you enough?
