About

Mallikarjun PM is an experienced Data Scientist with over 6 years of experience in the marketing and advertising industry. He holds a Master of Technology (M.Tech.) in Data Science/Knowledge Engineering from the National University of Singapore and an MBA from Quantic School of Business and Technology. Currently working as a Data Scientist at iag, Mallikarjun is involved in predicting home roof damage from drone images using Computer Vision and Deep Learning techniques. He is also working on geo-spatial data and using custom layers on mapbox to give a top-level view of the properties covered under IAG and to ease up the claims process. He has built data ingestion pipelines to ingest large-scale unstructured data for training machine learning models on AWS and also serves the model for inference. He is responsible for setting up machine learning training pipelines, model evaluation metrics, data labelling environment on AWS. His proficiency lies in Python, TensorFlow, Keras, AWS, GIS, Docker, Mapbox. Prior to this, Mallikarjun worked as a Data Analyst at ctrlshift where he was responsible for Campaign and Operational Analytics. He was the project technical owner for the company's operational business intelligence and created marketing effectiveness reports on Tableau using CRM data, advertising data (from DSPs), and 3rd party data for ad campaigns. He also developed ETL pipelines to ingest data from CRM tool (Salesforce), ERP tool (SAGE), and Google Sheets to data warehouse and implemented fault detection and recovery methods for minimum impact on failure. He was able to reduce man-hours required per report from an average of 2 hours to 5 minutes. His proficiency lies in Talend Open Studio Data Integration 6.4, PostgreSQL 9.6, Python 2.7, Tableau Desktop and Server 10.3, AWS RDS. Mallikarjun also worked as a Data Analyst Intern at hackertrail where he developed a Resume Information Extraction System to extract information from the resume and store details in a structured format. He also developed a resume classifier module using machine learning algorithms (SVM, k-NN and Naïve Bayes ensemble) which was earlier done through human inspection. He implemented Facebook Chatbot for candidates signup and job matching, reducing time to reach matching jobs by half and increased average candidate signups per day. He gained experience in text mining, information extraction, feature engineering, training and fine-tuning machine learning models, information retrieval, web crawling. His