Razorpay, a dynamic and innovative fintech company, is currently seeking an Analytics Specialist to join their team in Bangalore. This role calls for candidates with a strong educational background, requiring a Bachelor’s or Master’s degree in Engineering, Economics, Finance, Mathematics, Statistics, or Business Administration. With a prerequisite of 1 to 3 years of relevant experience, this opportunity is ideal for individuals looking to apply their expertise in a fast-paced financial technology environment. Proficiency in SQL and Python is essential for this role, as the Analytics Specialist will play a pivotal role in extracting meaningful insights from data to drive strategic decision-making.
Company Name: Razorpay
Job Title: Analytics Specialist
Qualification: Bachelor’s/Master’s degree in Engineering, Economics, Finance, Mathematics, Statistics, Business Administration
Experience Required: 1 – 3 Years
Job Location: Bangalore, Karnataka
The Role:
Analytics Specialist will work with the central analytics team at Razorpay. This will give you an opportunity to work in a fast-paced environment aimed at creating a very high impact and to work with a diverse team of smart and hardworking professionals from various backgrounds. Some of the responsibilities include working with large, complex data sets, developing strong business and product understanding and closely being involved in the product life cycle.
Roles and Responsibilities:
- 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 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 be closely involved with the product life cycle working on ideation, reviewing Product Requirement Documents, defining success criteria, instrumenting for product features, Impact assessment and identifying and recommending improvements to further enhance the Product features.
- 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 strategic goals of the organization.
- You will work with Business, Product and Data engineering teams for continuous improvement of data accuracy through feedback and scoping on instrumentation quality and completeness.
Mandatory Qualifications:
- Bachelor’s/Master’s degree in Engineering, Economics, Finance, Mathematics, Statistics, Business Administration or a related quantitative field.
- 1-3 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, 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, 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 approach to preprocess/cleanse data, dimensionality reduction.
- Work experience in Consumer-tech organisations would be a plus.
- Developed a clear understanding of the qualitative and quantitative aspects of the product/strategic initiative and leverage it to identify and act upon existing Gaps and Opportunities.
- Working Knowledge of A/B testing, Significance testing, supervised and unsupervised ML, Web Analytics and Statistical Learning.
