About

Harsh Ratde is an experienced Senior Data Scientist with close to 7 years of work experience in the broadcast media industry and retail. He is a strong engineering professional with a Master’s Degree focused on Energy Science and Engineering from Indian Institute of Technology, Bombay. Harsh has worked on a solution to improve business efficiency as well as on in-house Data science products adding to the backbone of core business and being utilized commercially day-in-day-out. He has worked on Media Mix Models, developed an Ad-Spot scheduling tool for media plan optimization for TV and Digital commercials, created individual-level viewership for linear TV, customer segmentation, and churn prediction for their OTT platform. Prior to this, he worked with a retail chain with Brick-and-Mortar and E-Commerce presence across various product categories covering pricing, image processing with deep learning algorithms, supply chain, operations and marketing related DS solutions. He helped in onboarding new users on 45% reduction in marketing spent during COVID using large datasets. Harsh is skilled in programming languages such as Python, R, SAS, Shell, and Git(basics), SQL. He is proficient in frameworks such as sk-learn, NLTK, Spacy, Tensorflow, PROC OPT model, BARON, GUROBI, and platforms such as SAS, AWS, and Google Cloud, Spark, and Kubeflow. He has additional skills in deep learning frameworks, development of REST API for production level deployment of AI/ML products, and others such as Data science, Technical, Analytics, and Databases. Currently, Harsh is working as a Senior Data Scientist at Damac Properties and is also hiring data scientists and MLOPS engineers. He has previously worked as a Data Scientist at Middle East Broadcasting Center and a Data Science Manager at Disney. At Middle East Broadcasting Center, he developed a distributed lag model to evaluate the ad-stock effect for each of the channel and impact on the show’s performance, reduced the marketing cost per show by ̃15%, and integrated various marketing channels including TV, Radio, Print, SMS Campaigns, Social Media, and Outdoors for performance of TV(target of improving viewership) and to onboard for subscription-based OTT platforms. At Disney, he integrated the price elasticity basis market share and competitor’s data, ad-positioning basis the cluster affinity of campaign and show, and developed a solution for optimized recommendation in sales for competitive pricing, saving channel inventory and reducing discount using branch and reduce (BARON)