Data science may have emerged as one of the most sought after professions in recent years, but the field demands lifelong learning and a sheer passion for solving real-world problems with data. This necessitates an exhaustive screening process to ensure that candidates are not only technically skilled but also come with an analytical bent of mind to successfully apply their learnings in a business setting.
Tesco follows a time-tested and elaborate process to find a suitable candidate for its data science team. Joining companies like IBM, LinkedIn and Infosys the supermarket giant has been using multiple assessors to adapt more specific and distinctive ways to thoroughly evaluate potential employees.
“Some organisations lay a lot of emphasis on previous work experience, which certainly is an important aspect, but should not be the sole reason behind selecting a candidate,” says Ranadheer Velamuri, Director – Tesco Technology at the company’s Bengaluru division. “It is equally important to evaluate them on other aspects, such as expertise, knowledge and solution-oriented approach,” he added.
In the news recently for announcing its commitment to honour all hiring offers rolled out during Covid-19 in its Bengaluru office, Tesco adheres to a ‘highly calibrated recruitment process’. Let us examine the details.
Tesco’s Hiring Process For Data Scientists
Spread over 4-6 weeks, Tesco’s recruitment process follows a rigorous and consistent assessment for data science candidates across varied core competencies. This includes technical, leadership and stakeholder management.
The initial screening process begins with assessing resumes, followed by a take-home technical exercise in which candidates are given a week to work through a series of practical data science problems.
“Our data scientists review these assignments and invite successful candidates for an interview loop, consisting of interactive discussions across key competency areas,” says Neha Chakrabarty, Head Recruitment at Tesco Bengaluru. “We are constantly refining our recruitment process to curate a streamlined experience for potential candidates,” she adds.
According to her, hiring for the data science team and building the talent pyramid demands targeted mapping and headhunting.
“This comprises three sets of talent pull – first comes the hunt for top-notch data scientists, then candidates with a medium-level experience, followed by newbies in the domain,” she says. “We ensure that we create a strong pipeline for each category and continuously engage with them throughout the year,” she adds.
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Tesco’s Ideal Data Science Candidate
As a centralised data science team that works across different business and technical domains, the company looks for a wide selection of skill-sets when recruiting for relevant positions.
So what does an ideal candidate at Tesco look like?
“We look for candidates with scientific mindsets, and those with a strong foundation in math, statistics and computer science,” says Velamuri. “Additionally, they should have a comprehensive understanding of machine learning algorithms, along with an in-depth acquaintance across different specialisations, such as operational research, NLP and computer vision,” he adds.
The company also emphasises on applied experience in Big Data environment or contribution to the end-to-end data science project lifecycle.
“We strive to take a balanced view of the potential candidate’s educational background as well,” says Chakrabarty. “Also, in the case of specialist roles, candidates need to have in-depth knowledge within the specific technical domain. This, combined with applied experience, is what makes an ideal data scientist at Tesco,” she adds.
The company primarily looks for potential candidates on prominent digital job boards, along with existing colleague referrals and targeted candidate mappings. “We are also open to networking events, conferences and rediscovering suitable candidates through mining across the existing database,” adds Velamuri.
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Role Of Data Scientists At Tesco
Successful candidates will join a team of over 50 data scientists and machine learning engineers. This team is primarily focused on the development of algorithmic product features and platforms which support, automate and optimise decision making, along with the remit to work across the entire business.
“These developments span across various technical domains, including machine learning and AI, operational research, statistics etc. and are dedicated to delivering real-world value at a large scale,” says Velamuri. “One such instance includes optimising the picking of billions of products and the delivery routes for millions of online orders every year. These advances help us reduce costs for our customers, increase delivery slot availability, and reduce environmental impact,” he adds.
According to the company, other areas of development include customising online search and recommendations, along with optimising the timing and scale of price reductions to lessen wastes.
“Our data scientists often work closely with our academia as well to drive cutting edge research, which aligns with our broader development portfolio,” adds Chakrabarty. “Fueled by Big Data and AI, data science is among the most desirable skills in the technology space today, and we have witnessed a keen interest in our data scientist positions across the globe,” she adds.
