Aakash Jadhav
About Aakash Jadhav
Aakash Jadhav is an Analytics Solution Developer currently working at Kinaxis in Toronto, Canada. He has a background in computer science and artificial intelligence, with previous experience at Tata Consultancy Services and Extrinsic Technology, among others.
Work at Kinaxis
Aakash Jadhav currently holds the position of Analytics Solution Developer at Kinaxis, where he has been employed since 2022. In this role, he focuses on developing high-quality analytics solutions and has contributed to the creation of prototype Databricks notebooks through various sprints in an Agile framework. His work involves collaboration with cross-functional teams to ensure effective delivery of analytics projects.
Previous Experience in Analytics and Engineering
Before joining Kinaxis, Aakash Jadhav gained extensive experience in analytics and engineering roles. He worked at Tata Consultancy Services as an Analyst from 2019 to 2021 and previously as a System Engineer for five months in 2019. He also served as a Machine Learning Engineer and Computer Vision Engineer at Extrinsic Technology from 2018 to 2021, where he focused on functional unit testing and data analysis.
Educational Background
Aakash Jadhav earned a Bachelor of Engineering in Computer Science from M.G.M's Jawaharlal Nehru College of Engineering, completing his studies from 2015 to 2019. He furthered his education by obtaining an Ontario Graduate Certificate in Artificial Intelligence and Data Science from Loyalist College, where he studied from 2022 to 2023.
Internship Experience
Aakash has held several internship positions that contributed to his professional development. He worked as a Database Management Intern at Lambentsoft Solutions for one month in 2018 and as an Internet of Things Intern at KitFlix for one year from 2018 to 2019. These experiences provided him with foundational skills in data management and IoT applications.
Hackathons and Projects
Aakash Jadhav participated in hackathons where he and his team automated a hardcoded forecast made by demand planners. This project enhanced efficiency and accuracy in forecasting, deepening his understanding of machine learning forecasting and its applications in supply chain management. He also worked on an analytics framework for retail and manufacturing domains, involving coding with JSON-schema and running docker workflows.