About

Sany Mathew is a highly skilled Data Scientist with over 6 years of experience in the information technology and services industry. With a strong engineering background and a Master of Information Systems Management focused in Business Intelligence and Data Analytics from Carnegie Mellon University - H. John Heinz III College, Sany has a demonstrated history of working on product development, experimentation, and diving deep to solve business problems. Currently working as a Data Scientist at Confluent, Sany has previously worked as a Senior Data Scientist at Pitney Bowes, where she led the Data Science efforts in the Sending Technology function of the company. She partnered with Product Managers, UX, Engineering Sr. Management to drive machine learning & advanced analytics projects, designed A/B tests and experiments, analyzed and communicated results to influence product changes, and architected product analytics efforts for the Sending Technology product suite. Sany also evangelized Data Science in the company, recruited, onboarded, and mentored Jr. data scientists & interns. In her previous role as a Data Scientist at Pitney Bowes, Sany collaborated with product managers and senior stakeholders to envision a reusable customer experience framework and build business-critical dashboards in Tableau and PowerBI. She applied time-series algorithms to analyze IoT signals and profile customer health based on meter usage, analyzed error logs to identify points of failure, and built machine learning models to predict errors. Sany also leveraged AWS technologies like EMR, DataPipeline, Kinesis to collect, aggregate, process, and visualize real-time meter telemetry data and evangelized Data Science in the company, recruiting, onboarding, and mentoring interns. Sany is skilled in Python, Tableau, AWS, PowerBI, R, and has relevant experience of 10.53 years as a Data Scientist and Research Scientist. She is passionate about product development, experimentation, and diving deep to solve business problems.