Rishikesan Ravichandran Email & Phone Number

New York, New York, United States

- Develop and deploy market demand models with GCP to Duetto to support real-time decision-making for optimised revenue management and pricing strategies.- Design and run experiments to optimize pricing decisions to drive CVR and profit-margins.

San Francisco, California, United States

- Reported to the Chief Data Officer to enhance direct market outreach by 10%- Improved data quality with third-party matching and unstructured data processing to drive market research, identify key areas, and strategize prospect outreach- Reduced reliance on third-party channels by 10% with predictive models like Logistic, Random Forests, and XGBoost to identify high-potential business loan prospects, enhancing revenue streams and reducing costs- Implemented a personalized email marketing campaign by automating information scraping and leveraging LLMs- Executed an A/B test to assess the efficacy of the framework and model, resulting in a 5% lift in the conversion rate

Bengaluru, Karnataka, India

Data Science • Accelerated executive decision-making by 3x by developing statistical models for scoring time series signals and creating real-time Grafana dashboards, enabling CXOs to anticipate risks and allocate resources effectively• Reduced unnecessary incident escalations by 20% using Bayesian anomaly detection (forecasting) and leveraged predictive models to build confidence, ensuring customers received timely and accurate updates Data Engineering • Cut data handling time by 30% by designing ML pipelines in Python (Pandas, NumPy), creating input/output modules for data integration, using PySpark for big data, and utilizing GitHub for CI/CD, enabling efficient data processing and storage• Improved workflow efficiency by 45% by using Airflow to automate ML processes for near real-time analytics, ensuring consistent execution and reducing failures• Optimized model performance tracking with MLflow and conducted A/B tests for models, resulting in a 10% improvement in incident detection accuracy and ensuring high-quality models in production• Enhanced business monitoring by setting up scalable scoring and alerting platforms with Docker and deploying microservices on Azure and AWS, reducing deployment times by 30%Product Development • Reduced manual root cause analysis time from 3 hours to 15 minutes by leading the development of vuRCABot, automating workflows, generating incident reports, and creating actionable insight cards for efficient incident management

Geelong West, Australia

Developed a mathematical model using the Evolutionary Algorithm and FEA to design water coolant channels on a tool incorporating existing design guidelines

Master Of Science - Ms, Business Analytics, 3.88/4

Activities and Societies: Beta Gamma Sigma Honour Society, Dean’s Scholarship, MSBA Fellowship

Bachelor Of Technology - Btech, Mechanical Engineering, 3.81/4

Activities and Societies: Distinction with Honour