About

Aditya Vikram Singh is an Applied AI and Data Engineering professional with a wealth of experience in implementing cutting-edge ML models to improve decision-making and mitigate losses. He is currently working for the global risk and fraud modelling team at JP Morgan, where he is responsible for leading end-to-end enterprise projects. Aditya's core strengths lie in implementing next-gen research papers from scratch, building credit risk/fraud detection models over a billion data points, and designing end-to-end systems for machine learning pipelines. Aditya has a strong background in data mining, predictive analytics, recommendation systems, and NLP problems. He enjoys solving complex problems and providing deep insights into the impact of various factors on business outcomes. Aditya has experience working with huge datasets in R/Python and Hive, and he is skilled in feature engineering, preprocessing, validation, model deployment, and real-time monitoring. Aditya has won several accolades for his work, including being ranked #1 in the EdgeVerve ML hackathon, #2 in the All India Sangam-IIT Madras ML hackathon, and being among the top 100 in the ZS young data science challenge. He has worked on highly confidential and crucial projects, including creating data pumps that work at petabyte scale in real-time and batch processing, building behavioral models to predict customer propensity, and developing anti-fraud toolkits for internal usage. Aditya has a Bachelor of Engineering in Civil Engineering from BIT and completed his senior secondary education at Delhi Public School, Dhanbad. He has a diverse tech stack, including research scientist, data scientist, ML, AI, Python, Spark, NLP, SQL, recommendation systems, Kafka, computer vision, Hadoop, ETL, Apache Hive, deep learning, AWS, object detection, infra, CNN, object detection, big data, Pytorch, integration, and mobile. With almost six years of relevant experience, Aditya is a skilled and accomplished professional in the field of Applied AI and Data Engineering.