Karthik Ramasubramanian

All that matters is, how big you wanna dream and how hard you wanna work

- Anonymous

Karthik Ramasubramanian

Deputy General Manager
Mahindra & Mahindra
Over seven years of practice and leading Data Science and Business Analytics in Retail, FMCG, E-Commerce, Information Technology for a multi-national and two unicorn startups. A researcher and problem solver with a diverse set of experience in the data science lifecycle, starting from a data problem discovery to creating a data science prototype/product. On the descriptive side of data science, designed, developed and spearheaded many A/B experiment frameworks for improving product features, conceptualized funnel analysis for understanding user interactions and identifying the friction points within a product, designing statistically robust metrics and visual dashboards. On the predictive side, developed intelligent chatbots which understand human-like interactions, customer segmentation models, recommendation systems, identifying medical specialization from a patient query for telemedicine and many more.
Machine Learning Python Time Series NLP
  • Mahindra Rise
    Deputy General Manager
    2018 - Current
  • Probyto
    Senior Data Scientist
    Probyto is building scalable research driven Data Solutions for solving problems for industries, society, and government across the globe. We build minimal working prototypes of our solutions and build a use case for clients. Probyto's capability is at a cross junction of data science applications and industry. Our experienced solution partners learn industry problems and create solutions. A scalable and easily deployable solution in data science. We help clients save money and increase productivity by reducing go-to-market time drastically. Probyto currently has capabilities in Telemedicine, Home Automation, Energy, Law, Agriculture, Talent Management, Pharmaceutical, and E-Commerce. Our team is working on Blockchain solutions, Robotic Process Automation, Internet of Things (IoT) Devices, training advanced solutions to corporate clients and Open Data Lakes. I am an experienced data science professional leading a team of data scientist, data engineers, and data solution architect to create rapid prototypes to support Probyto growth story in India. Also helping Probyto members firms in Ireland and the USA to convert prototypes into industry solutions at scale. I have spent more than 7 years in Data Science domain, working for Hike, Snapdeal and RB, mentoring and teaching at platforms like Springboard & Edureka and published numerous papers and a book. I also handle the academic relations for Probyto in India Actively participate in Analytics related thought leadership, authoring, public speaking, meet-ups, and training in Data Science as a contribution to Industry. Industry Expertise: Consumer Products, E-Commerce, Information Technology and Big Data Analytics Current areas of interest: Machine Learning Algorithms, Data Product Frameworks, Internet of Things (IoT), BlockChain, Robotic Process Automation and Image Content Analysis
    2017 - 2018
  • Apress
    Book: http://www.springer.com/fr/book/9781484223338 This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data. The book provide the width of concepts from statistical learning paradigm to machine learning world. Selective datasets are fitted into multiple scenarios, and even results with poor fit are also kept ( as that's the reality, all models are not good).Some advanced topics are also scratched; - Sampling by rejection - Filter/wrapper/embedded methods for feature selection - Bootstrap sampling - Ensemble - More than 12 type of visualisation including spatial visualization - Microsoft Cognitive Services API bases text analysis - Ensemble and hyper-parameter optimizations in R - Basic of neural nets, also deep learning example on H2O - Scalable technologies to explore For in-depth reading references are provided and high level description of R package is also provided. A good reference guide for Quick Starters in Machine Learning!
    2016 - Current
  • Hike Messenger
    Growth Analyst
    1. AB Experiments for improving product features around stickers - The planning for such experiments involves creating the hypothesis, defining success metric, choosing appropriate control and test groups, and deciding the sample size based on statistical significance. Every new feature is rigorously tested before rolling it out to end customer. 2. Funnel Analysis for sticker recommendations and custom camera - Understanding user interactions are critical for the success of a feature in any given product. Funnel analysis gives away the friction points within a feature and helps product team take a corrective action to eradicate the same. If the new feature is too complex, we might see major drops in the funnel from start to end of the user interaction. 3. Defining key feature metrics and creating visual dashboards - Worked closely with product teams to understand and formulate the key metrics which needs continuous monitoring and helps in course correction whenever required. It also helps in keeping the entire product stay relevant to users preferences and track the growth of the product.
    2016 - 2017
  • Snapdeal
    Business Analyst II
    In my position as Business Analyst – II in the Data Science team of Snapdeal, I have been working on some key projects to re-define the companies long-term goals and objectives. The projects includes: 1) Tracking and planning the retention of Snapdeal’s core customer base. 2) Designing, implementing and improvisation of product recommendation engine and customer segmentation model. 3) Designing user campaigns for increasing customer repeats and retention. 4) Development of models for customer churn and forecasting daily active user. 5) Devising new pricing strategies for improving net margins. Some of the projects: Customer Segmentation a. Developed customer segmentation model for targeted E-Mail and SMS campaigns. b. The model resulted in improved campaign design and high order conversion. Daily Active User (DAU) Predictor a. A multiple linear regression model for predicting the number of DAUs based on the amount of discounts and category margins was developed. b.The predictor gave an estimate of the effect an e-commerce platform would have if discounting on products were increased or decreased. Suitably the budget for sales day and discounts can be allotted. Customer Churn Model a. A model was developed for predicting the probability of customers to repeat within certain number of days of their last transaction. b. The model is useful to understand the improvement areas and help company to focus on key strategies for customer retention. Gender Prediction Engine (GPE) a. Developed a python based tool for predicting the customer gender based on their name (Indian names). A powerful web scrapper was designed for extracting Indian names for the initial corpus. b. The tool helped to understand the purchase behavior of male and female customers and suitably design targeted campaign
    2015 - 2016
  • RB
    Business Analyst
    Technical Lead for Sales Application in India. The key responsibilities includes: 1) Designing and implementing solutions for existing gap's in the application and process. 2) Data Analysis 3) Rolling out new components as per changing business needs. 4) Managing every new enhancement and bug fix.
    2011 - 2015
  • Smart and Secure Environment Lab
    Research Intern
    Worked in Network & Computer Security, Machine Learning, Data Mining. Designed and implemented algorithms for extracting portable executable headers in win32 .exe files. Created machine learning models with features extracted from network traffic data, human voice samples and computer virus, worms and trojans
    2009 - 2011
  • PSG College of Technology
    Master of Science (M.Sc.), Theoretical Computer Science
    2010 - 2012
What I do outside work
  • Fitness
  • Travelling
  • Learning

Programs I mentor

I 'instruct', 'teach', 'solve doubts' and do 'hands-on activities'

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