Partnerships Analytics Internship

Razorpay is hiring for the role of Partnerships Analytics Intern!

Responsibilities of the Intern:

• You will work with large, complex data sets to solve open-ended, high-impact business problems using data mining, experimentation, statistical analysis and related techniques, machine learning as needed
• You would have/develop a strong understanding of the business & product and conduct analysis to derive insights, develop a hypothesis and validate with sound rigorous methodologies or formulate the problems for modeling with ML
• You would apply excellent problem-solving skills and independently scope, deconstruct and formulate solutions from first principles that bring outside-in and state-of-the-art view
• You would expedite root cause analyses/insight generation against a given recurring use case through automation/self-serve platforms
• You will develop compelling stories with business insights, focusing on the strategic goals of the organization
• You will work with Business and Data engineering teams for continuous improvement of data accuracy through feedback and scoping on instrumentation quality and completeness

Requirements:

• Bachelor's/Master’s degree in Engineering, Economics, Finance, Mathematics, Statistics, Business Administration or a related quantitative field
• Minimum 2-3 years of high-quality hands-on experience in analytics and data science
• Hands-on experience in SQL and Python
• Define the business and product metrics to be evaluated, work with engg on data instrumentation, and create and automate self-serve dashboards to present to relevant stakeholders leveraging tools such as Tableau, Qlikview, Looker etc.
• Ability to structure and analyze data leveraging techniques like EDA, Cohort analysis, and Funnel analysis and transform them into understandable and actionable recommendations and then communicate them effectively across the organization.
• Hands-on experience in working with large-scale structured, semi-structured and unstructured data and various approaches to preprocessing/cleanse data, dimensionality reduction
• Working Knowledge of A/B testing, Significance testing, supervised and unsupervised ML, Web Analytics and Statistical Learning.