Data Science Hiring and Interview Process at Purplle
Online beauty and personal care brand Purplle became India’s 102nd unicorn after raising $33 million last year in Series E funding, led by South Korea’s Paramark Ventures. Founded by Manish Taneja and Rahul Dash, the Sequoia-backed company has also found investors in Premji Invest, Blume Ventures, and Kedaara Capital. With a small but strong 11-member team, consisting of seven data scientists and four data analysts, Purplle stands out in its industry by harnessing the power of AI and analytics to refine the customer experience and drive operational excellence. The team employs data-based prognostic models to empower other business divisions in making concise and more impactful choices. Data science is applied across various key functions and areas at Purplle. These include personalisation, product discovery, search and suggestions, recommendations, and data-driven supply chain management processes like box suggestions and DRR (delivery, return, and replacement). Analytics India magazine got in touch with Vivek Parihar, Head of Engineering, Purplle, to understand how the e-commerce platform leverages data science in their daily operation and what they look for in potential candidates. Read more: This AI Startup From Paris Raises Highest Seed Funding Ever Inside Purplle’s AI & Analytics Team “One of the main focuses of Purplle’s AI and analytics team is to provide personalised recommendations and optimising the search engine, resolving customer product discovery challenges through the use of AI and analytics,” said Parihar. Purplle ensures that the appropriate products are displayed to the correct customers at the appropriate time, optimising their platforms by using personalised and customer-centric data. Moreover, AI is employed to tackle supply chain concerns, such as determining the most suitable source warehouse for efficient delivery. One recent achievement of Purplle’s data science team is taking control of Purplle’s item views (IVs) planning process, transitioning it from a manual to a fully automated system. This allows for the prediction of product-level IVs, enabling the business teams to take informed actions and streamline their processes in collaboration with brands. By planning daily IVs, the algorithm effectively targets customers and optimises their experience. Additionally, the introduction of a reinforcement learning-based algorithm, specifically the multi-armed bandit algorithm, has enabled Purplle to strike a balance between exploration and exploitation. This algorithm determines when to show the most performing products and when to showcase diverse products for exploration purposes. Their tech stack is on Google’s cloud platform, including tools like Bigtable, BigQuery, Google Storage, Dataproc, Kubernetes, Redis, and Elastic Search, among others, to train and serve their data models. The team also uses Flagr and Open Source for AB testing, and Python as a programming language. From model building and deployment to serving, their entire pipeline is fully automated using CI/CD. Interview Process Purplle seeks individuals who possess robust analytical capabilities, a deep understanding of statistics, proficient programming skills, keen business acumen, and expertise in data visualisation techniques. Purplle’s interview process for data science roles consists of two rounds for junior data scientists and three rounds for senior data scientists. Round 1: The first round is conducted with senior data scientists to assess a candidate’s technical proficiency and problem-solving abilities. Round 2: The second round takes place with the data science lead to delve deeper into the candidate’s domain knowledge and project experience. For senior-level positions, an additional round takes place with the senior data product manager to focus on the candidate’s leadership skills and ability to contribute to the strategic vision of the team. Finally, the “Level Up” round is included in the cultural fit assessment to evaluate candidates’ cultural fitment, ensuring alignment with the organization’s values and ability to thrive in the work environment. Data science candidates are assessed based on key result areas (KRAs) or key performance areas (KPAs), which include evaluating the impact of their projects on business metrics, producing reusable artefacts, ensuring code and delivery quality, proactively resolving errors, optimising code, implementing new technologies or learning initiatives, maintaining consistent effort variance below 10%, managing bugs and issues, and achieving optimal performance in memory handling, CPU usage, and query optimisation. Expectations When joining Purplle’s data science team, new members are expected to value independence and excel in analysing and making decisions that positively impact the business and sales. Proficiency in coding, familiarity and strong graph with commonly used ML frameworks are essential technical skills. The team actively seeks individuals who can effectively apply their ML knowledge to evaluate, understand, and solve real-world business problems. On the other hand, candidates can expect to work in a dynamic environment where they can make decisions autonomously, communicate freely, build and test hypotheses, and experiment. However, Vivek points out that some of the common mistakes that candidates make often include applying models and expecting them to solve business problems directly without intervention or analysis, and utilising complicated models without knowing the model mechanism and function clearly. Work Culture At Purplle, the company maintains an employee-centric work culture with flexible policies, differentiating itself from competitors in the industry. The work environment fosters collaboration among teams, facilitated by frequent meetings with reporting managers and teammates, ensuring seamless communication throughout the organisation. The company boasts a flat organisational structure and a vibrant startup culture, actively cultivating cross-functional team connections and providing access to anyone within the organisation, regardless of their level or designation. When it comes to working with the data science team, Purplle stands out by fostering individual ownership and empowering employees. They use advanced data science tools and robust pipelines to capture user activity, providing hands-on experience with the latest technology. “The team is knowledgeable, resourceful, and ready to provide guidance. We promote cross-team learning and idea exchange. We encourage experimentation and analysis before going live, leading to valuable knowledge and rapid learning” he added. When it comes to perks, employees receive performance-based ESOPs after a stipulated period. The company also offers insurance, maternity leave, and other benefits like employee discounts on the Purplle shopping app. Candidates applying to Purplle should remember to master the basics and focus on applying their knowledge to solve business problems instead of building fancy models to showcase their expertise,” he concluded. So if you have a penchant for analytical thinking, problem-solving, and a keen interest in working with data, you are ideally suited for Purplle. Read more: Data Science Hiring Process at Meesho
With a small but strong 11-member team, consisting of seven data scientists and four data analysts, Purplle stands out in its industry by harnessing the power of AI and analytics to refine the customer experience and drive operational excellence
With a small but strong 11-member team, consisting of seven data scientists and four data analysts, Purplle stands out in its industry by harnessing the power of AI and analytics to refine the customer experience and drive operational excellence
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Shritama (she/her) is a technology journalist at AIM who is passionate to explore generative AI with a special focus on big techs, database, healthcare, DE&amp;I, hiring in tech and more.