![]() ![]() PayPal addresses such pitfalls by evaluating candidates based on their approach to solve real-time problems and implementing the solutions using a small dataset, and exposing the candidate to PayPal’s values, people, work and work culture. Not exposing the candidates to real-time business problems and consumer impacting decisions.Not following a consensus-driven approach.Late hire is always better than a wrong hire. Not doing due diligence and completing all key fundamentals of the hiring process.“Data scientists, in general, go around with a hammer in their hand looking for a nail, and that is the biggest challenge,” he said.Ĭhandramouliswaran highlighted the typical mistakes in data scientists’ hiring process: ![]() Other challenges include a lack of understanding of the digital payments/fintech ecosystem’s nuances and strong business acumen. Interview process: Post the screening, the candidate goes through an average of five rounds of interviews, each lasting 45-60 minutes covering the fundamentals of data science, ability to program and work with data, ML Libraries (for engineering DS roles) and also address a business case on analytics or model development.Ĭhandramouliswaran said one of the critical challenges in recruiting is the limited availability of talent for niche skill sets such as NLP and deep learning. The business team then spends about 30-45 minutes explaining the roadmap, challenges and cultural fitment. Screening by talent acquisition and business teams: The first step is screening by the talent acquisition team, who spend about 20-45 minutes explaining the role and understanding the fitment. The interview process at PayPal consists of two significant steps: Candidates who have worked with big data, built data science models, solved pressing business problems are preferred.” We look to hire talent that constantly challenges us and pushes us to innovate every day.” Interview Process “For us at PayPal, skills and educational background are not as important as experience and exposure,” added Chandramouliswaran. “However, we have never focused on hiring only from top-tier institutes and are happy to provide opportunities to top talent with the right skill set and passion for solving large scale problems and driving global impact.” In terms of educational background, PayPal looks for candidates with programming, statistics, economics, and mathematics background with a focus on logical reasoning, data interpretation and a programming mindset. On the business side, experience in payments, banking, risk, customer management, marketing experience is a huge plus. Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions. “We also recruit many Business Analytics individuals for whom the data science functional experience requirement is less onerous,” said Chandramouliswaran. On the functional side, preference is given to candidates with machine learning skills, OpenCV and deep learning. PayPal mainly focuses on two critical areas while hiring data scientists - functional skills and business. ![]() Director, Global Financial Crimes and Customer Protection & Chennai Centre Head, PayPal India, to understand the hiring process for data scientists at the company. ![]() On the other hand, Global Data Science, Risk Analytics, Workforce Planning and Forecasting, Merchant & Consumer Analytics are focused on enabling Business/Risk/Compliance and work in a global construct. For instance, Customer Support Intelligence focuses on research and development to solve NLP issues at scale. PayPal, one of the largest online payment processing firm globally, has several data science teams working as Center of Excellence (CoE). ![]()
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