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. 

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.

• Machine Learning Expertise: Demonstrated understanding and practical experience in applying machine learning techniques to real-world business problems, including:

• 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.