Yogesh Kumar
About Yogesh Kumar
Yogesh Kumar is an Associate Data Scientist at Innoplexus, specializing in data science techniques for the pharmaceutical and finance sectors. He has a background in Computer Science and has held various roles in data engineering and software development since 2017.
Work at Innoplexus
Yogesh Kumar has been employed at Innoplexus since 2020 as an Associate Data Scientist. In this role, he specializes in applying data science techniques to address complex challenges within the pharmaceutical and finance sectors. Prior to his current position, he worked as an Intern Data Scientist for two months in 2017 and as a Data Engineer for nine months in 2019. He also served as an Associate Software Engineer in Financial Services for eight months from 2018 to 2019. His extensive experience at Innoplexus highlights his progression and expertise in data science.
Education and Expertise
Yogesh Kumar earned a Bachelor of Technology (BTech) in Computer Science from Motilal Nehru National Institute of Technology, studying from 2014 to 2018. He also completed his Intermediate education at Kendriya Vidyalaya, focusing on Physics, Chemistry, Mathematics, and Computer Science from 2001 to 2013. His educational background provides a strong foundation for his expertise in data science, particularly in feature engineering for both structured and unstructured data.
Background
Yogesh Kumar has a diverse background in data science and engineering. He began his career at Innoplexus, where he undertook various roles that allowed him to develop his skills in data crawling, extraction, and normalization. In addition to his technical roles, he served as a Training and Placement Representative at his university, which provided him with insights into the industry and enhanced his communication skills. He also briefly worked as a Content Strategy Analyst at The Indian Economist.
Achievements
Yogesh Kumar contributed to a publication focused on improving the reliability and reducing the cost of task execution on predictive models, which was published by Springer. This work demonstrates his ability to engage with academic research and apply his findings to practical challenges in data science. His skills in automating data pipelines and enhancing efficiency in data processing tasks further highlight his contributions to the field.