Staff Analytics Specialist

Razorpay was founded by Shashank Kumar and Harshil Mathur in 2014. Razorpay is building a new-age digital banking hub (Neobank) for businesses in India with the mission is to enable frictionless banking and payments experiences for businesses of all shapes and sizes. What started as a B2B payments company is processing billions of dollars of payments for lakhs of businesses across India.

We are a full-stack financial services organisation, committed to helping Indian businesses with comprehensive and innovative payment and business banking solutions built over robust technology to address the entire length and breadth of the payment and banking journey for any business. Over the past year, we've disbursed loans worth millions of dollars in loans to thousands of businesses. In parallel, Razorpay is reimagining how businesses manage money by simplifying business banking (via Razorpay X) and enabling capital availability for businesses (via Razorpay Capital).

The Role

The Staff Analyst role is a senior individual contributor position that emphasizes strategic impact, advanced analytical rigor, and cross-functional collaboration. You will focus on product analytics, taking ownership of high-impact projects, mentoring other team members, and contributing to the organization’s strategic goals through actionable insights and innovative analytics solutions.

This role combines hands-on data work with thought leadership, leveraging advanced analytics techniques, structured problem-solving, and storytelling to guide product decisions and strategy.

Key Responsibilities

Strategic Analytics Leadership

• Lead the definition of structured, scalable methodologies to solve high-ambiguity, open-ended product and business problems.
• Identify and prioritize analytics opportunities to enhance product performance, customer satisfaction, and business growth.
• Develop long-term strategic plans and next-big-bet ideas for product enhancements, aligned with organizational goals.

Advanced Data Analytics

• Conduct end-to-end analyses, from data extraction to insights presentation, using advanced statistical methods and machine learning techniques as needed.
• Establish robust frameworks for experimentation and impact assessment of product changes, ensuring statistical rigor and actionable recommendations.
• Develop dynamic and modularized data solutions, including advanced dashboards, to improve visibility and decision-making across teams.

Product Collaboration

• Partner closely with product managers to define success metrics, instrument new features, and evaluate product performance post-launch.
• Influence key decisions by effectively integrating analytics into the product lifecycle, from ideation to feature optimization and data products integration.
• Promote and uphold best practices in instrumentation, experimentation, and impact evaluation within the team and across the organization.

Mentorship and Influence

• Mentor and coach junior analysts, fostering a culture of continuous learning and excellence in analytics.
• Guide teams on advanced technical skills, including SQL optimization, Python-based data manipulation, and innovative statistical/machine learning approaches.
• Advocate for and implement analytics innovations, fostering a self-serve analytics culture and enhancing team productivity.

Stakeholder Engagement

• Communicate compelling narratives from complex data insights to senior stakeholders, influencing strategic decision-making and ensuring alignment with organizational goals.
• Act as a trusted advisor to business and product leaders, abstracting complex challenges into actionable insights and driving stakeholder confidence.

Required Qualifications

• Education:

  Bachelor’s/Master’s degree in Engineering, Economics, Statistics, Mathematics, Computer Science, or a related quantitative field.

• Experience:

  7+ years of hands-on experience in analytics, with a proven track record in solving high-impact business problems in consumer tech or fintech.

Technical Proficiency:

• Advanced SQL skills for large-scale data manipulation.
• Proficiency in Python for data analysis, automation, and machine learning.
• Expertise in data visualization tools such as Tableau, Looker, or Power BI.
• Strong understanding of statistical concepts, A/B testing, and causal inference.
• Familiarity with core concepts such as supervised and unsupervised learning, feature engineering, model evaluation metrics, and model deployment.
• Experience implementing models such as regression, classification, clustering, and tree-based algorithms
• Knowledge of best practices for scaling and integrating ML solutions into production environments in collaboration with engineering/data science teams.
• Knowledge of data pipeline architecture and experience with tools like Airflow or similar DAG orchestration frameworks.
• Business Acumen: Deep understanding of product lifecycle, KPIs, and business strategy in consumer tech or fintech contexts.
• Experience leading cross-functional projects with significant organizational impact.