Australian Bureau of Meteorology, Sydney Research Suppport Scientist
FEB 2023 - PRESENT
Worked on implementation of multivariate bias correction method – Multivariate Recursive Nested Bias Correction (MRNBC) for the Australian Climate Service. Developed a Python wrapper for the MRNBC bias correction method originally written in FORTRAN. Later, implemented a parallel framework for execution of bias correctio on large-scale climate datasets using Dask parallelization on NCI Gadi.
Quince, Hyderabad— Data Scientist-2
MAR 2022 - AUG 2022
Worked on data science use cases for improving customer retention by identifying key drivers of repeat behaviour. Built and deployed regressive tree models for predicting and optimising logistics cost in online retail. Interpreted model predictions using Explanable AI (XAI) methods (SHAP and LIME).
3Qi Labs, Hyderabad— Data Scientist
NOV 2019 - NOV 2021
I have worked extensively with machine learning and data mining tools on Big Data technologies to generate insights into the data. My current project includes developing Deep Learning model to detect issues in data such as referential integrity failure and outlier detection. I am also responsible for generating insights into the data using Exploratory data analysis and Visualisation tools. Furthermore, my responsibilities include analysing code for system testing, debugging and created test transactions to find, isolate and rectify issues. Tensorflow, Elasticsearch, Kibana, Hadoop, and Tableau are few of the other tools I work with extensively.
Bomotix, Hyderabad— Machine Learning Developer
JAN 2019 - NOV 2019
As a machine learning developer, my job involved end-to-end developeent and deployment of various Deep Learning pipelines in Apache MxNet using Kubernetes and Docker containers. Object detection, object tracking and human pose estimation are a few of the Computer vision problems I worked on during this period. Moreover, I developed new procedures for requirements gathering, testing, scripting and documentation to strengthen quality and functionality of these applications.
The University of Sydney, NSW Australia— Research Intern (Machine Learning)
JUN 2018 - AUG 2018
During my internship, I worked with Dr Rohitash Chandra and Prof Sally Cripps on various research projects in the area of Bayesian machine learning. I worked on method formulation for training Neural Networks using MCMC sampling schemes. As an outcome of this internship, I learnt the principles of Bayesian Machine learning and how it can be used effectively to train neural networks. Three of our research papers are now published in Top tier Journals. I continue to work with Dr Chandra on various Research projects involving Neuroevolution via MCMC.