Sunil Gowda
About Sunil Gowda
Sunil Gowda is a Data Scientist specializing in customer success at o9 Solutions, Inc. in Bangalore, India, with prior experience at Cognizant and a background in Mechanical Engineering from PES College of Engineering.
Work at o9 Solutions
Sunil Gowda has been employed at o9 Solutions, Inc. as a Data Scientist in the Customer Success department since 2021. He is based in Bangalore Urban, Karnataka, India. In this role, he applies his expertise in data science to enhance customer satisfaction and drive successful outcomes for clients.
Previous Experience at Cognizant
Before joining o9 Solutions, Sunil worked at Cognizant as a Data Scientist from 2018 to 2021. During his tenure, he contributed to various data-driven projects, utilizing his skills in machine learning and data analysis to support business objectives. His experience at Cognizant spanned three years in Bengaluru, Karnataka, India.
Education and Expertise
Sunil Gowda earned a Bachelor of Engineering (BE) degree in Mechanical Engineering from PES College of Engineering, studying from 2010 to 2014. His educational background laid the foundation for his analytical skills and understanding of engineering principles, which he later applied in data science.
Background in Business Analysis
Prior to his role at Cognizant, Sunil served as a Business Analyst at CREATISE from 2015 to 2018. In this position, he gained valuable experience in analyzing business needs and translating them into actionable insights, further enhancing his analytical capabilities and understanding of data-driven decision-making.
Technical Skills and Specializations
Sunil Gowda possesses a strong mathematical understanding of machine learning algorithms and their implementation. He specializes in dimensional reduction techniques such as Principal Component Analysis (PCA) and is proficient in time series forecasting methods including ARIMA, SARIMA, UCM, LSTM, and multivariate forecasting. His expertise also includes survival analysis using the Kaplan-Meier survival function, outlier detection, and feature engineering techniques.