With data science emerging as a popular career choice in the 21st century, the hiring market in the space has become increasingly competitive. A cursory search on popular job portals will tell you that there is a large pool of existing and aspiring data scientists in India alone.
This may indicate that the supply of data scientists should match the growing demand for these positions. However, the ground reality tells another story. Data science is a dynamic field that demands constant learning, and available talents often fall short of the requirements companies seek. What is more, the applicant pool shrinks even further given that many companies are concurrently hiring for the same skill sets.
Cloud-based software company Freshworks has often found themselves in a constant tussle with other organisations to find the best data science talent, and its carefully curated team is testament to the fact that many of the top talents in the market see value in working here.
Acquiring data science talent, albeit challenging, has been a rewarding experience for the company as it embarks on its next wave of innovation in the B2B SaaS product space.
Comments Suman Gopalan, Global CHRO at Freshworks:
“AI and ML will have an increasingly pivotal role in what we do, both to develop Freshworks’ future product portfolio, as well as to ensure that we are constantly increasing the value proposition for our customers.”
Skills Expected Of Data Scientists At Freshworks
The hiring process at Freshworks is designed to test the technical skills, design capabilities, problem-solving abilities and cultural fit of candidates. The company also lays emphasis on onboarding a diverse set of data science aspirants with a background in Computer Science, Statistics, Applied Mathematics, Operations Research, and Advanced Economics.
“We focus on attracting talent who believe in our vision and are passionate about solving the challenges we are trying to solve,” says STS Prasad, Executive VP – Engineering at Freshworks. “Beyond educational background, we also ensure that each applicant has adequate skills, along with the commitment to be successful,” he adds.
When it comes to identifying their recruitment needs for data science, AI and ML, the company typically hires across the full spectrum – Data Scientists, ML Engineers, Data Platform Engineers, and Data Analysts, to name a few.
“We are always looking for talent who are experienced in designing and developing hyperscale data and computational systems, big data applications and pipelines,” says Gopalan.
Some technical skills that can prove valuable in data science interviews at Freshworks are:
● ML Frameworks: Scikit-learn, TensorFlow/Pytorch/Keras, Spark/Distributed Programming
● Techniques: NLP, Image Recognition, Neural Networks, Clustering, Supervised/Unsupervised Classification (XGBoost, Random Forest), Dimensionality Reduction, Advanced Regression, Bayesian Inference, Feature Engineering
● Platform Engineering: ML platform/pipeline development, large scale model deployment using cloud-based architecture
● Big Data: Near real-time big data applications and pipelines developed on Hadoop/Spark, Teradata or other statistical packages
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Hiring & Interview Process
The company integrates technical rounds to test for knowledge on data structures and algorithms required for specific job positions. Additionally, when hiring for senior levels, it also assesses strategic acumen, managerial leadership, thought leadership, and ability to create an impact within the organisation when recruiting for its data science positions.
“The first round usually reviews the background and experience of the candidate and their problem-solving skills,” says Prasad. “Here, the candidate is often asked to define AI solutions to identify which customers are likely to buy the product,” he adds.
In this round, interviewers have time to ask open-ended questions that assess the candidates’ thought process. It also leaves room for candidates to ask follow-up questions.
“The second round focuses on design and architecture to assess the candidate’s technical depth and their ability to scale solutions through sustainable architecture, etc,” says Gopalan. “Oftentimes, the interviewers challenge a candidate’s assumptions to assess their reasoning ability to support their existing design. Additionally, senior-level candidates are asked to present how they would solve complex real-life scenarios,” he adds.
Non-Traditional Ways Of Hiring Data Scientists
Like some companies including Mindtree, Infosys and CSS Corp, Freshworks also employs non-traditional means to straddle between hiring for specific needs and opportunistically hiring specific individuals. Some of these unconventional hiring methods to tap into talented professionals include engaging in research partnerships with academic institutions.
“These partnerships with academia are more of a symbiotic relationship, where the research students get their research sponsored by Freshworks, and they, in turn, focus on solving our business problems,” says Prasad. “We also focus on building networks by participating in technology events and local meet-ups. We are always on the lookout for great talent, regardless of the time of year,” he adds.
Besides these, the company also sources candidates from job portals, peer networks, and through external consultants who help them identify potential talent. Given the niche of the talent pool, it also urges employees to refer friends, professional contacts and acquaintances whom they know would fit a particular role that is open.
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Common Hiring Mistakes In Data Science
According to Freshworks, a common error across the data science landscape is hiring candidates who are academically focused but do not have enough practical experience.
“Since data science, as a function, largely tends to overlap with research, some applicants might expect the job to be research-focused, while the job role might require the candidate to apply knowledge, crunch the data and solve complex industry problems,” explains Gopalan. “If applicants do not demonstrate the motivation to solve complex industry problems, there is a good chance that they will struggle to be successful in their role of developing fit-for-purpose, AI-first solutions,” he adds.
As a side note, Gopalan also feels that companies need to make sure that they effectively communicate to candidates what the job roles and responsibilities are, “which is especially important as many employees will transition from the academic world.”
Opportunities For Data Scientists At Freshworks
“Our employees have an enormous role in furthering our AI-powered vision across all our products,” begins Prasad. “In a real sense, they are helping chart the future success of not only our company but of every single organisation that uses our products,” he adds.
Data scientists at Freshworks, depending on their specific job requirements, are positioned to contribute towards developing cutting-edge Freddy (the company’s AI engine) capabilities across sales, support, marketing, and HR. This includes AI-powered lead scoring, automated workflows, conversational AI, response recommendation to agent and customer queries, insights and big data analytics.
Furthermore, the company also constantly looks to widen the scope of its research projects, providing employees opportunities to work with the brightest minds in the industry. For instance, it has partnered with reputed institutes like the Robert Bosch Centre for Data Science and AI in IIT Madras to bolster its AI capabilities.
“At Freshworks, we deal with a large volume of data, with over 250,000 businesses using our technologies,” says Gopalan. “Aspiring data scientists have access to a wealth of data in their quest to find answers to how companies are better engaging with their customers,” he adds.
