Krishna S.

Krishna S.

Senior Software Engineer

Bengaluru, India
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About Me

I am a Data Scientist at Toptal, where I apply my curiosity and passion for analytics and machine learning to solve challenging problems in e-commerce, marketing, and natural language processing (NLP). With four years of experience in machine learning, including having been a part of the analytics division of JP Morgan Chase & Co., I have developed and deployed data-driven solutions for various clients across different domains and industries. Some of the projects I have worked on include building an NLP pipeline in Python using Keras to extract and summarize opinions and feedback from customer product reviews, performing market mix modeling, demand forecasting, and A/B testing analytics to optimize spending, supply chain, and marketing funnel, and building data pipelines and dashboards in AWS, GCP, and Tableau to provide actionable insights and data-driven decision making. I have a strong engineering background, with a Bachelor of Technology in Electrical and Electronics Engineering from the Indian Institute of Technology Gandhinagar, one of the premier institutes in India. I have also completed multiple certifications in machine learning, Python, and software testing from Coursera, Udemy, and ISTQB. I am a great communicator, enthusiastic developer, and a lifelong learner. My goal is to leverage data and machine learning to create value and impact for businesses and society.

Machine Learning 4 years Python 3 Analytics Artificial Intelligence (AI) Code Review Data Analytics Statistical Data Analysis Algorithms Data Reporting Deep Neural Networks Exploratory Data Analysis Python Deep Learning SQL Statistical Learning

Work history

Weather Company
Sr Machine Learning Consultant
2024 - Present
Remote

Developed and implemented first-party audience models tailored for various user segments such as leisure and business travelers. Created deterministic and random forest models to accurately classify leisure and business travelers. Utilized advanced data science methods to enhance the targeting and personalization capabilities of the ad platform. Employed self-supervised deep learning techniques for age bucket prediction.

Organifi
Data Scientist
2020 - 2024 (4 years)
Remote

  • Developed and deployed an NLP pipeline to extract and summarize opinions and feedback from customer product reviews.

  • Built data pipelines in AWS and GCP for a reporting and analytics data warehouse.

  • Built numerous executive summary dashboards in Tableau by identifying key metrics to track goals specific to individual teams.

Python 3 GPT Natural Language Processing (NLP) Generative Pre-trained Transformers (GPT) Sentiment Analysis Data Analytics BigQuery Google Cloud FunctionsGoogle CloudTableauTableau Desktop Pro MySQLAmazon Web Services (AWS) AWS Lambda Amazon RDSData ScienceTechnical Hiring Code ReviewInterviewing Source Code Review SQLMachine LearningPythonDeep Learning
JP Morgan Chase & Co
JP Morgan Chase & Co
Analyst
2019 - 2019
Remote

  • Designed and built a next-generation merchant acquisition tool in R Shiny for a credit card business.

  • Provided pricing analysis of credit card business.

  • Built and integrated a minimum revenue model based on customer demographics.

TableauPythonRCode ReviewSource Code Review SQLMachine Learning
Tredence Analytics
Business Analyst
2018 - 2019 (1 year)
Remote

  • Segmented retail customers based on their shopping behavior by using random forest.

  • Designed, built, and deployed an end-to-end machine learning pipeline.

  • Performed marketing analysis of a leading retail company in the US.

TableauRPythonInterviewing Source Code Review Task Analysis Data ScienceSQLMachine Learning
Capgemini
Software Analyst
2017 - 2018 (1 year)
Remote

  • Scraped the web for collecting unstructured data present on a website.

  • Created and deployed various executive summary dashboards.

  • Automated data cleaning pipelines to save significant person-hours every week.

PythonMySQLLinuxTask Analysis

Portfolio

Multi-modal Fully Convolutional Network for Semantic Segmentation

A fully convolutional network (FCN-32s) trained to semantically segment forest scene images with RGB and nir_color input images.The project was developed to help unmanned drones in smooth navigation. The model is trained and tested on still images of forest scenes.I used Intel Edison and Microsoft Kinect for proof of concept and prototype creation.

Smart Medical Network

I worked on a smart medical network for Intel ESDC 2016, Shanghai. The project aimed to create an ecosystem of a medical network that stores the clinical and real-time data of patients for smoother and quicker diagnosis in an emergency.

Education

Education
Bachelor of Technology Degree in Electrical Engineering
Indian Institute of Technology Gandhinagar
2013 - 2017 (4 years)