Lead Data Scientist

About the Role

We are looking for a data scientist to join our Data Science team at GoPay - you will create models which help grow the customer base and transactions in GoPay. You will work closely with the business, product and engineering stakeholders to identify and solve some of the most challenging business problems in the payments space. You will have the opportunity to work in a fast-paced environment in a highly cross-functional team and leverage our robust data infrastructure

What You Will Do

• Work as part of a cross-functional team of engineers, product managers and business analysts to build data science solutions that build customer engagement and help grow GoPay’s customer base and transactions
• Rapidly prototype data science solutions and be involved in product and feature discussions
• Analyze large volume data and generate insights which will be actionable
• Own end-to-end solutioning, from formulating the technical problem to deployment (along with engineers) of the solution
• Participate in internal and external conferences and workshops

What You Will Need

• 6-8 years of relevant experience
• Strong quantitative and problem-solving skills
• Solid understanding of Statistics/ML fundamentals and demonstrated experience in Python, R
• Communication skills to engage with business stakeholders to understand their needs and effectively communicate the results of the analytical solutions
• Ability to write clear and concise technical documentation
• Ph.D. or Masters in a quantitative discipline is a plus point
• Experience in recommender systems, ranking, search and discovery is a plus

About the Team

GoPay Data Science team builds critical ML components/models which go into the engineering systems which make GoPay a safe, trusted and fun way to do payments. Our team members come from varied backgrounds, and bring with them a wide set of skills (mathematics, statistics, machine learning, deep learning etc) which we use some of the toughest business problems in GoPay. We are enthusiastic about both data science techniques and methods, as well as the business impact of our models, and have numerous internal forums where sharing, discussions and presentations by the team members happen.

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