About

Mandar Kulkarni is an Analytics Solutions Associate at JPMorgan Chase with a passion for data science. He completed his Bachelor's degree in Computer Applications from Pune University and his Master of Computer Application from Vishwakarma Institute of Technology. With a strong skill set in Python, Core Java, R programming, SQL, NLP, and Flask, he has also worked on cloud platforms including Heroku and AWS. Mandar has excelled in various data science competitions, including achieving a rank of 31 (top 2%) in the Kaggle competition for the project "NLP with Disaster Tweets" and securing position 42/6805 in the Analytics Vidhya-Hackathon for the "Loan Prediction Challenge" competition. He has also participated in the "Spot the face mask challenge" competition by ZindiWeekendz and secured position 10. Mandar has published an article on "Recruitment Prediction System" and has implemented multiple end-to-end data science projects. His GitHub and Kaggle profiles showcase his expertise in data science, NLP, SQL, AWS, XGBoost, Python, Flask, Big Data, Java, ML, and Search. Prior to his current role at JPMorgan Chase, Mandar worked as a Big Data Python Developer at Barclays and as a Machine Learning Intern at Tata. During his internship, he worked on a live project in machine learning called "Plate detection & counting," where he created models with both YOLO and Tiny-yolo in Python, achieving an accuracy of 0.82. Overall, with 1.70 years of relevant experience, Mandar is a dedicated and skilled data science professional with a passion for solving complex problems.