How to get hired as a data scientist
Data science is being touted as the hottest career option in the 21st.Century. McKinsey Global Institute projects that there will be somewhere between 140,000 and 180,000 data scientist jobs open by 2018 in the US alone.
And keeping in line with this forecast the need for data scientists in India has exploded as well..
India’s big data market is expected to hit US$ 1 Billion in 2015. This is in line with the global trend where the global big data market is expected to hit a whopping US$ 8 Billion.
So the need for data scientists is immediate, not just in India but globally. A demand that is unlikely to abate in the near future.
As it is with any new field that becomes main stream, the hype factor is high and every tech professional looking for a job in big data is now suddenly somehow a ‘data scientist’. In this article, we cut through the clutter and discuss how mid career tech professionals considering a career in big data, can help fill this demand. The focus is on mid career techies, since we want to focus on people who can help fill the immediate demand for ‘experienced’ data scientists.
We discuss the traits that differentiate the good data scientists from the rest. . How they can go about picking up the essential skills. Where they can expect these jobs to come from and what potential employers look for.
The makings of a good data scientist
Today, a number of academicians, economists, physicists, mathematicians, statisticians, data miners and programmers are exploring careers in data science. In making this transition, the qualities that differentiate the successful ones are as follows:
First and foremost, they are masters of 2 essential disciplines, i.e. programming and statistics together with strong domain knowledge. They know early on that to become a successful data scientist they need to be good at several subject disciplines.
Secondly, a passion for data is important to be a successful data scientist. Not only to be adept at working with data, but to be appreciative of data itself as a first-class product; and the ability to organize and analyze huge amounts of formless data.
Thirdly, technical shortcomings do not deter budding data scientists from their search for creative ways to solve a problem. They find creative methods to visualize information in order to identify clear and compelling patterns in the data.
A good data scientist = data hacker + programmer+ analyst+ coach+ story teller+ artist.
No doubt a powerful combination of skills that is rare to come by.
So how do you become a great data scientist?
The most important aspect of this position is the science of it all. So we should start with what it means to be a scientist. Being a data scientist means you have to obtain, scrub, explore, model and interpret data. In reality, there are a less number of people who possesses the skills to be an ideal data scientist.
To start off a data scientist’s most basic, universal skill is the ability to write code. You must be a competent programmer, if not this job is not for you.
You then have to know where your current gap is, whether it is programming, statistics or domain.
Here’s a tip: one of the best ways to pick up these missing skills is to find a way to work with people who do have those skills. You have to get creative about this and work on projects with a ‘mentor’, where you can get your hands dirty with all that data. You also have to get on an accelerated path and explore data beyond your comfort zone, to rapidly learn about new technologies and methods.
Now that I am ready, where do I look?
The need for data scientists today is in almost every industry. The immediate demand for big data related positions is in industries such as mobile, health care and financial services. Whereas, industries that depend solely on data, such as ad tech, are likely to have a continuous hiring demand for big data-related positions.
Finally, what is the potential checklist an employer might use to check me out?
- Can he or she program? They do not have to be a top notch programmer, but they need the ability to quickly prototype algorithms. Have they shown the potential to quickly learn new technologies and techniques?
- Can they craft a story from a data set and communicate the key data findings? Can they visually and verbally represent and communicate the numbers they work with?
- Are they disengaged from the business world or do they know how their work may apply to and solve your management challenges?
- Do they have a favourite analytics methodology and how do they keep their skills sharp and fresh. Have they done any advanced certification track courses connected to data science? Have they contributed to open source communities, or do they have a portfolio of personal projects (on Github or Ipython Notebooks)?
Keep the data conversations flowing
In conclusion, whether in India or in the world over, successful data scientists must realise that the value they bring to the table is the ability to keep a steady and continuous conversation with data. A conversation that keeps executives and product managers within an organisation informed, on the implications of data for products, processes and decisions. And of course guides the enterprise on how to monetise this data.
Authored by Abir Barua – People Orchestrator at Crayon Data
Part of the founding team at Crayon Data, Abir Barua is based in Singapore. Prior to his role as People Orchestrator, he performed multiple roles such as Product Owner, Sales and Client Management at Crayon.
During the course of his career, Abir spent 22 years in Advertising, and worked with top-notch agencies like Y&R and Ogilvy. Abir was head of Y&R Korea (International Division) for four years and has handled marquee MNC clients like Citibank, KFC, Toyota; and local clients in Singapore like Singapore Telecom and DBS Bank.
Abir played competitive rugby and thinks it is the best team game in the world to build character. A voracious reader, he is especially partial to books by Asian authors. His personal philosophy is to question everything and to always take the road less travelled.