The hiring team at IT services giant Infosys succinctly describes the company’s ideal data science candidate as one who brings the right mix of technical and domain expertise, along with good communication skills. But with the problems associated with differentiating between what a candidate writes on their resumes, versus what their actual skills may be, the hiring process becomes a tad bit more challenging.
With almost two decades of experience developing organisational strategies and analysing human resources issues at Infosys, Richard Lobo, Executive VP & Head HR at the tech giant, has seen the company grow – in both their data science requirements, as well as the methods they adopt to fulfill them.
Does the company, like some, still prioritise a candidate’s educational background when hiring for these roles?
“While practical, working domain knowledge and technical skills still remains the most important criteria in the selection process, educational background is also taken into consideration, especially for junior-level candidates,” says Lobo. “This ensures that we take a balanced perspective when hiring data scientists,” he adds.
According to him, relevant certifications also help, especially because additional qualifications allow candidates to demonstrate their learning. With a host of data science certifications made free amid Covid-19 lockdowns, this may be a good time for aspiring candidates to upskill themselves and be better prepared for the interview process at Infosys.
Hiring Process Of Data Scientists At Infosys
Along with hands-on coding experience on R, Python and SAS, the company looks at a candidate’s ability to scale up in cloud technologies and problem-solving skills.
“Our hiring process usually involves screening and a preliminary shortlist, followed by technical and personal interviews and further discussions as needed,” says Lobo. “We also check references and background information provided to us before proceeding with hiring,” he adds.
The candidate’s experience and the projects they have worked on forms the basis of these interviews. Lobo says that a lot of emphasis is placed on the techniques used to solve business problems, the ability to understand and analyse data, and the overall problem-solving approach taken by the candidate.
“We also look at the candidate’s comfort level and approach with coding syntax by giving them real-life business problems,” says Lobo. “Additionally, we also look at certain behavioural aspects like team working skills, client handling as well as communication,” he adds.
Non-Traditional Recruitment Approach
Like some other companies, Infosys conducts hiring throughout the year to engage with potential talent based on requirements. Going beyond conventional means, the company consistently explores non-traditional ways to hire data science professionals.
“We actively network with the right talent in various conferences, hackathons, and other events, and alert them whenever a suitable opportunity emerges,” says Lobo. “We also continue to focus on training and reskilling as a source for talent, as the industry demand will continue to evolve,” he adds.
In addition to this, Infosys also emphasises on maximising the relevant profile inflow from employee referrals and applications to their website. Furthermore, the company also works through their social and professional media channels for their recruitment needs. “For rare skills, we do look at hiring through professional firms as well,” says Lobo.
According to him, despite these efforts, the demand for exceptionally good data scientists usually is higher than the supply. How does the company, then, overcome this deficit of talent?
“We have an excellent training infrastructure and ability to deploy learning and upskilling in a large way,” says Lobo. “We use a balanced approach of attracting and retaining the best, as well as building up our talent pipeline through training,” he adds.
Common Mistakes When Hiring Data Scientists
As a hiring manager, it becomes extremely important for Lobo and his team to onboard the right talent for such a critical function.
“Since data science is a trending professional field, we need to differentiate between those who have actual expertise from those who might have basic knowledge,” explains Lobo. “This becomes an interesting challenge because it is preferred that we do not have a client deployment of someone who is not professionally ready for the role,” he adds.
The potential to solve business problems using technology and the probability of someone bridging both worlds effectively is virtually limitless. With that, then, what does a successful candidate do once hired?
“Junior candidates or candidates with less industry exposure are expected to scale up on new technologies, exhibit flexibility to absorb instructions and gain experience that makes them ready for higher roles,” says Lobo. “Mid to senior-level candidates, on the other hand, should demonstrate the ability to understand client requirements, lead teams and grow the business,” he adds.