Job Description

Senior 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: Work with large, complex data sets to solve open-ended, high-impact business problems using data mining, experimentation, statistical analysis, and machine learning as appropriate. Develop a deep understanding of the business and product to identify opportunities where GenAI and data-driven solutions can create impact; formulate hypotheses and validate them using rigorous analytical methods. Build and deploy modular, production-grade Python code to support use cases including prompt engineering, LLM-powe'red tools, and process automation using GenAI APIs. Design and optimize data pipelines and workflows to support both traditional analytics and GenAI model integrations. Apply strong problem-solving skills and first-principles thinking to independently scope and structure analytical solutions, integrating the latest in GenAI and ML where relevant. Drive automation and scalability of recurring insight generation, using GenAI or conventional approaches to reduce manual effort and enable self-serve insights where possible. Maintain high standards in project management, including clear scoping, timeline ownership, and proactive communication. Mandatory Qualifications: bachelors/masters degree in Engineering, Economics, Finance, Mathematics, Statistics, Computer Science, or a related quantitative field. 4+ years of high-quality hands-on experience in analytics, data science, or ML-based product development. Proficiency in Python with experience writing modular, reusable code; prior experience integrating GenAI models via APIs (eg, OpenAI, Claude, Gemini, etc) and building LLM-powe'red applications. Hands-on experience with SQL and working with structured, semi-structured, and unstructured data. Experience designing or maintaining data pipelines and workflows (eg, using Airflow, dbt, or equivalent). Experience applying A/B testing, significance testing, and both supervised and unsupervised ML techniques. Prior experience in consumer-tech or product-oriented organizations is a plus. Strong communication and storytelling skills with the ability to influence stakeholders through data and logic. Good to have: MLOps & Infrastructure: Build and maintain automated pipelines for model versioning and deployment (for example, using Datarobot, Airflow, Github). Responsible AI: Set up checks for bias, fairness, and harmful outputs in all LLM-powe'red features. Cloud & Scaling: Deploy and scale Python services on AWS (Lambda, ECS/EKS).