Neethu Mariya

Data Scientist at J.D. Power

Neethu Mariya has a range of work experience in data science and engineering. Neethu currently works as a Data Scientist at J.D. Power since May 2021. Prior to this, they worked at Manulife as a Data Engineer (Co-op) from May 2020 to August 2020. During their time at Manulife, they developed a pipeline using Python and Bash scripting to automate data extraction and preparation processes, saving significant manual effort. Neethu also implemented an ETL process using metadata tagging, reducing manual effort by 40%. Neethu also has experience as an Operations Clerk at THE FEDERAL BANK LIMITED from June 2017 to August 2019. In this role, they worked on strategic projects that improved customer satisfaction and achieved business KPIs. Neethu also led a team in a digitization campaign, resulting in a 17% increase in profit for the bank's division.

Neethu Mariya completed their Master's degree in Management Science from the University of Waterloo from 2019 to 2020. Prior to that, they obtained a Bachelor of Technology (BTech) degree in Electronics and Communications Engineering from the Federal Institute of Science and Technology (FISAT) from 2013 to 2017. Their secondary education was completed at St. Patrick's Academy, Angamaly from 2011 to 2013, with a focus on Science.

In addition to their formal education, Neethu Mariya has also obtained several certifications. Neethu received a Python certification from TestDome in April 2021. In March 2021, they obtained a certification in Finance Foundations: Risk Management from LinkedIn. Furthermore, they achieved certification in Advanced SQL from Kaggle in February 2021, and in SQL Essential Training from LinkedIn in January 2021. In May 2020, they completed a certification in Learning Linux Shell Scripting from LinkedIn. Beginning in January 2020, they undertook a series of certifications from LinkedIn, including Applied Machine Learning: Algorithms, Applied Machine Learning: Foundations, Learning Python, and Artificial Intelligence Foundations: Machine Learning, all of which were completed by November 2019.

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