Out of the 7 billion + people in the world, around 30% don’t have access to vision correction services. Meanwhile, half of Indians (530 million) need vision correction, but only 170 million have them.
Lenskart was founded with a vision to help people see the beautiful word by revolutionising the eyewear category in India and beyond. The ten-year-old company now delivers 7 million + spectacles a year in India and Singapore and plans to ship out over a billion glasses over the next ten years.
“AI and Tech are the core of Lenskart’s DNA with key innovations in our 3D-Try on and omnichannel digital and store model. By scaling up our core data science team, we are looking forward to transforming our customer experience and business as we expand not only in India, but also across Singapore, the Middle East and the US,” Amit Chaudhary, co-founder, Lenskart, told Analytics India Magazine.
Analytics and data science are core to Lenskart’s business across several aspects: retail & store analytics, digital & e-commerce analytics, marketing analytics, supply chain analytics, customer service, and more. Lenskart recently hired Saurabh Agrawal as the Head of Analytics & ML. Since his appointment, Saurabh has aggressively looked to expand the data science team, roping in data engineers, business analysts, data visualisation experts and data scientists to build the analytics foundation at Lenskart.
We got in touch with Saurabh to understand how the data science team is structured, the hiring process and more.
“Lenskart being an omnichannel direct to consumer retail brand provides a unique ecosystem of a full spectrum of analytics use cases which very few companies in the world provide. This creates a unique challenge and opportunity for professionals to learn and contribute, at the same time solve a 2 billion+ problem,” he said.
Data Science Team At Lenskart
The data science team at Lenskart is centralised and enables all critical business functions across manufacturing, retail, digital, marketing, sales, operations, customer service and HR. “While the DS team works centrally, we are deeply embedded with the functional teams and have a very strong say in the operational strategy,” said Saurabh.
The data science team at Lenskart solves key business problems across various functional domains. Therefore, to be part of the data science team, the candidate is expected to work across building expertise in 1-2 subject areas, understand the domain and applications of analytics. “Our team members are involved in data science use cases from start to finish. It gives them a kick to see the analysis they do being implemented and derive business impact,” he said.
“We see that more than 60% of our customers in-store is digitally influenced. Understanding customer preferences and needs are crucial for us. We have a big focus on unlocking omnichannel analytics which requires us to understand the complex online – offline customer journey and use multi touch attribution method for driving marketing effectiveness”, he said.
Lenskart has a cloud-first tech approach. The tech stack comprises technologies such as data lake — built out of AWS, Power BI for visualisation, Google Analytics and Clevertap for digital analytics. For data science, it uses R & Python and a few other automation frameworks. The data science team at Lenskart are expected to work around these technologies.
Skill Sets Required
Saurabh said their key focus while recruiting data scientists is on foundational skills, a strong learning mindset and aptitude. Lenskart looks for a strong business understanding, math, statistics, computer science and programming skills.
Soft skills are crucial too. “Since our team is required to work with cross-functional teams, there is an equal focus on collaboration skills and good communication, especially the ability to explain analytics in simple language,” he added.
The most important thing Lenskart focuses on is the passion for transforming customer experience and business with data and algorithms.
Lenskart looks for candidates with a background in computer science, engineering (B.tech & M.tech) and MBA from reputed colleges. “Strong grit, learning and innovation mindset always get preference,” he said.
“We believe in the philosophy that your number of years of experience matters less to us. Your performance matters more,” he said.
Interview Process
Data science recruitment at Lenskart is a mix of lateral hiring, fresher hiring and cross movements in the company and other industries. Most of the sourcing happens directly and from LinkedIn and job sites. “We had some of our best people who took a career move internally as well from tech and product teams,” he said.
The data science interview process at Lenskart involves the following steps:
- Round 1 – Introduction and Technical Round
- Round 2 – Detailed technical round, case study/machine test.
- Round 3 – Leadership round
- Round 4 – HR
Currently, the company is hiring for domains such as data engineers, data science & ML, BI & business analyst across NCR and Bengaluru region.
Lenskart has a broad range of roles within the data science team and across the company. It provides employees opportunities to try different roles in the data science team across tech, product and business functions.
Hiring Mistakes
Saurabh said one of the critical mistakes is hiring only data scientists when an effective data science team needs a team with full spectrum of roles. It is like playing an orchestra, you need all instruments in the band and having them work in harmony is key to have wonderful music.
The team at Lenskart is been scaled up with strong focus on diversity, not only gender, but people with different domain background spread across NCR and Bangalore. “While analytics is a heavy logical right brain work, we are consciously focusing equally on left brain aspects of visual design & empathy helping us drive next level team effectiveness “.
Saurabh said the ultimate goal of analytics and data science is to enhance customer experience and solve business problems. However, many people think learning Python is equivalent to learning analytics.
“The passion for solving problems using data and algorithms is the most important thing one needs to discover within,” he said.
