Arunima Ray
About Arunima Ray
Arunima Ray is an Assistant Manager in Decision Science at HSBC, with a background in data analysis and statistics. She has experience in developing statistical algorithms, managing cloud services, and utilizing various coding solutions for real-time analytics.
Work at HSBC
Arunima Ray currently serves as an Assistant Manager in Decision Science at HSBC, a position she has held since 2021. In this role, she focuses on data-driven decision-making processes and analytics. Her responsibilities include developing strategies that leverage data insights to enhance operational efficiency and support business objectives. Arunima's expertise in statistical analysis and real-time data processing contributes to the bank's analytical capabilities.
Previous Experience as Data Scientist
Before joining HSBC, Arunima Ray worked as a Data Scientist at Global IDs from 2019 to 2021 in Kolkata, West Bengal, India. During her tenure, she was involved in various data science projects that required advanced analytical skills. Her experience included managing cloud services on AWS and Azure platforms, which enhanced her proficiency in cloud-based data solutions.
Experience as Data Analyst at MachineSense
Arunima Ray began her professional career as a Data Analyst at MachineSense, where she worked from 2016 to 2019. In this role, she developed self-created statistical algorithms for data analysis, which improved the efficiency of data processing tasks. Her work in Kolkata, West Bengal, India, laid the foundation for her expertise in handling complex data sets.
Education and Expertise in Statistics
Arunima Ray earned her Master's degree in Statistics from the University of Kalyani, completing her studies from 2011 to 2016. This integrated M.Sc program provided her with a strong foundation in statistical methods and data analysis techniques. Her academic background supports her current roles in data science and decision-making.
Technical Skills and Specializations
Arunima Ray specializes in handling JSON format streaming data for real-time analytics. She integrates coding solutions using R in cloud environments and Raspberry Pi, showcasing her technical versatility. Additionally, she employs edge analytics with Raspberry Pi and Linux for data processing. Her proficiency in using R Shiny for data visualization projects further enhances her analytical capabilities.