Embrace a career in AI, the millennial way
LEARNING Five professionals tell us what an AI job actually involves—and none of it requires building killer robots
From the world’s largest tech companies to start-ups, everyone is looking for people well-versed with Artificial Intelligence (AI). But a career in this business is no cakewalk: A lot of mathematics, constant leaning and understanding human behaviour are just some of the ways to get a foothold in this fast-growing industry. We spoke to five AI professionals, who tell us that a career in this field is about many different things, from data analysis, text and image recognition to linguistics—and no, evil robots do not figure in the list.
ROHIT GHOSH
lives in a very real way,” he says.
Compensation can range from ₹10-₹12 lakh per annum for graduates with no experience, going up to ₹15-₹35 lakh per annum for professionals with a couple of years of experience
SOHAN MAHESHWAR
robots. It’s about speech data, linguistics, and these are just a subset of AI technologies,” he says.
“Interacting with developers every day, and teaching people to build stuff is exciting,” he says.
Compensation can range from ₹10-₹60 lakh per annum.
WHAT HAPTIK LOOKS FOR IN AI PROFESSIONALS
Aakrit Vaish, 32, CEO and founder of Ai-based chatbot platform Haptik, takes hiring interviews very seriously. “You need to be very, very good at maths to be good with AI, because this field essentially involves looking at a problem, understanding aggregated data, and making sense of it. It’s about predictions and solving problems,” he says. Vaish says 50% of any interview conducted by his start-up is based on a case study. “We judge a candidate’s problem-solving skills. We also like to look at specific experience in data science, like modelling work or algorithm design, and then ask specific questions on them,” says Vaish. A practical familiarity with AI helps—try using chatbots or Alexa at home.
PAUL MEINSHAUSEN
processes, his choice of career was a logical step. Meinshausen started work in this field early in his career when he worked with the US army (2009-2011) on a series of projects to understand human behaviour in conflict areas like Iraq and Afghanistan. “This wasn’t somewhere I could go out to interview and observe people. So, I began working with large intelligence data sets instead,” says Meinshausen. Eventually, he went on to study data science as a researcher at Harvard and become a data scientist fellow at Chicago University, studying statistics and programming. After stints at analytics firm Teradata in Singapore, and Housing.com and Paysense in Mumbai, Meinshausen now works at a Bengaluru-based early-stage venture fund Montane Ventures.
“That AI is man versus machine. At a chess or Go game where a human plays against a computer, you see one person with a cup of coffee playing with a machine. But behind the machine is a team of at least a 100 scientists and so many pieces of systems, all built by people who capture data, clean it, build feedback loops and implement it,” he says.
“I get to work with interesting start-ups and model the world by looking at its different pieces for the problem I am working on,” he says.
Starting salaries range from ₹10-20 lakh per annum and can go up to ₹25 -40 lakh per annum for 5-10 years of experience.
SIX LESSONS FROM A POST-MILLENNIAL ON GETTING A JOB IN AI
As a graduate it is not easy to get a job in AI, unless you can show the right experience,” says Shavak Agrawal, 21, who works as a data scientist with Microsoft . A bachelor of engineering in computer science from BITS Pilani, Agrawal made sure he opted for machine learning courses during his degree. He had a summer training stint as a research intern at IBM Labs, Bengaluru, and a second one with Quant One Technologies, a Kolkata-based firm that develops trading algorithms. For his final- year project, he worked for six months with Flipkart in Bengaluru, building AI frameworks to identify and predict anomalies in the e-commerce company’s supply chain. Here are six things he learnt:
• Introspect early about why you want to get into machine learning. This is something interviewers ask and you should have an answer for it. For me, this was triggered by the famous Target store case study, where an algorithm on shopping patterns uncovered that a young woman was pregnant even before her family knew. • Keep yourself constantly updated on the latest research. I follow Arxiv.org, which publishes the latest research papers, and computer scientists like Yoshua Bengio and Yann lecun, besides blogs like Distill.pub.
• Do online courses.the machine learning courses on Coursera by Andrew Ng, co-founder of Coursera and adjunct professor at Stanford University, is a great start. • Spend time on understanding algorithms and how they really work , instead of accepting the algorithm as a black box.
• Be selective about the kind of job you choose. Today there are AI jobs advertised everywhere, but you need to look carefully and in detail at the kind of work or product that is being worked on and at the background of the people working on it. It’s important to have good mentorship—someone who can teach you on the job.
• Have a good understanding of linear algebra, probability, statistics and other core maths concepts. Machine learning involves complex functions with different variables. You tweak different parameters You should be comfortable doing that. Companies will have to realize that skilling is an imperative and not just a buzzword. Moreover, it impacts all levels unsparingly. Other industrial waves had impacted the blue-collared in particular. Not this one. The Fourth Industrial Revolution will force even the white-collared to re-skill at a very fast clip. Companies will have to map progress over a measurable time horizon and be impartial in assessment. There are a host of training programs but companies will have to assess the ones which suit their specific needs. And of course, it’s not skilling for skilling’s sake but it should lead to enhanced employability. More and more Public Private partnership in the areas of skill building would improve employability immensely. Business / hiring managers always look for a 100 % fit. HR’S role is to set expectations and Face to Face interviews at best, can get the interviewer a strong gut feel about a candidate. It is also true when they look at resumes which more often than not are couched, don’t reveal an accurate picture. However, numbers don’t lie. Of course, statistics can also be massaged to make people look like rock-stars but in general, it can reveal a truer picture. Data science is based on the idea that almost everything reveals a pattern, though may be a very complex one at times so why should human behavior be left out of its ambit? Talent hiring is now considered as strategic, and leaders now want to move away from the “gut feel” approach to be more data-centric in their decision making. It also helps to normalize the data and smoothen out the spikes. That’s where analytics come into play.
Secondly, identification of right talent from the market goes way beyond the keyword search in the CV. It needs an intelligent Job creation is a factor of the overall economic growth. Unlike China, our domestic demand lies latent and hasn’t been serviced adequately. There’s huge potential there and it’s for policymakers to ensure that servicing the domestic demand is as lucrative as exports-led growth. Also, there’s a severe mismatch between talent available and talent which is employable. Our course curricula even in toptiered colleges also needs a fresh look in order to improve employability factor. • Ability to discard legacy if required. No more of parroting “the golden days.” • Learnability, and very quickly
at that.
• Ability to work in a matrix structure, which simply put, is about multi-tasking. Working with diverse stakeholders. • Willingness to fail. But fail
quickly, pick up and move on. • Our “Digital Atma” is in social media – ability to conduct oneself in the social sphere too where the glare is constant. Particularly, not spreading Fake News which is a modernday menace.
• Ability to align with the dynamic environment around in terms of organizational philosophy, technology framework