Sandeep Kr Mishra
About Sandeep Kr Mishra
Sandeep Kr Mishra is a Principal Data Scientist at Digital.ai, where he specializes in machine learning and data analysis. He has a strong background in predictive maintenance, IT analytics, and advanced data science techniques.
Current Role at Digital.ai
Sandeep Kr Mishra currently serves as a Principal Data Scientist at Digital.ai, a position he has held since 2022. In this role, he focuses on advanced data science methodologies and machine learning applications. His work contributes to the company's initiatives in predictive maintenance and IT analytics, leveraging his extensive background in data analysis and machine learning.
Previous Experience at Progress
Before joining Digital.ai, Sandeep worked at Progress as a Data Scientist from 2014 to 2018 in Bangalore. During his tenure, he developed expertise in various data science techniques and contributed to projects that enhanced the company's data analytics capabilities.
Educational Background
Sandeep holds a Bachelor of Technology (B.Tech.) in Computer Science & Engineering from Dr. A.P.J. Abdul Kalam Technical University, where he studied from 2008 to 2012. He furthered his education by obtaining a Master of Technology (M.Tech.) in DSE from the Birla Institute of Technology and Science, Pilani, completing his studies from 2018 to 2020.
Technical Skills and Expertise
Sandeep possesses hands-on expertise with various machine learning libraries and tools, including SparkML, TensorFlow, and PyTorch. He applies association rule mining methods such as Apriori and FP-Growth in his data analysis work. His skill set includes clustering techniques like K-Means and hierarchical clustering, as well as advanced natural language processing techniques, including BERT and Transformer models.
Focus Areas in Data Science
Sandeep specializes in several areas within data science, including predictive maintenance, IT analytics, and automation in machine learning. He employs advanced techniques such as anomaly detection, feature selection, and hyper-parameter tuning. Additionally, he utilizes optimization techniques, including stochastic optimization algorithms and neural networks, to enhance model performance.