Well-qualified Data Scientist experienced working with vast data sets to break down information, gather relevant points and solve advanced business problems. Skilled in predictive modeling, data mining and hypothetical testing. Offering more than 4 years of experience in data visualizations and model building.
Overview
5
5
Languages
8
8
years of post-secondary education
9
9
years of professional experience
Work History
Business Presentation Specialist
McKinsey & Company
Trivandrum, Kerala
07.2015 - 07.2019
Managed multiple projects with high degree of accuracy and attention to detail to attain exceptional quality visualizations in tableau and powerpoint.
Offered friendly and efficient service to all customers, handled challenging situations with ease.
Was promoted to a grade 2 specialist within 9 months of hiring to be one of the fastest in the branch's history
Worked on incredibly sensitive and ground breaking data and projects with the industry leaders of various fields
Internship Student
Dr. Oetker
Melbourne, Victoria
07.2020 - 11.2020
Established and implemented Market Basket Analytics and Sales Forecasting Techniques
Created a Web application for the Marketing team to instantly analyze frequently bought products and expected product sales
Researched and compiled tailored analytics and reports for all senior management with python and Tableau.
Compiled next step recommendations for the company to take
CSP Specialist
Intercontinental Exchange Inc.
Hyderabad, India
08.2021 - Current
Managed and Optimized Internal Server Databases (CSP): Led the optimization and management of internal CSP databases to ensure data integrity, performance, and accessibility, directly supporting analytics and reporting across different business units. This initiative streamlined data consolidation from multiple servers into a unified reporting mechanism, enhancing operational efficiency and strategic decision-making.
Strategic Collaboration with Business Stakeholders: Engaged closely with business stakeholders to comprehend their data analysis needs regarding internal server performance and utilization. Provided critical insights derived from CSP data, facilitating informed strategic planning and operational enhancements, thus ensuring alignment with organizational goals and client needs.
Data Governance for CSP Data: Instituted robust data governance policies tailored to the CSP environment to maintain the accuracy, completeness, and reliability of server data. This framework underpinned the reliability of consolidated reporting mechanisms, ensuring stakeholders had access to trustworthy data for making strategic decisions.
Forecasting and Predictive Analysis Using CSP Data: Applied statistical models and machine learning algorithms to CSP data to predict future server trends and behaviors. These insights were pivotal in proactive decision-making, aiding the committee in planning server farm constructions and decommissioning outdated servers based on accurate forecasts of server needs and usage patterns.
Development and Delivery of Training Programs: Spearheaded the creation and delivery of training programs focused on BI tools and data interpretation techniques specific to CSP data. This initiative fostered a data-driven culture within the organization, empowering end-users with the knowledge to effectively utilize CSP reports for operational and strategic purposes.
Creation of Documentation and User Guides for CSP Reporting Tools: Authored comprehensive documentation and user guides for CSP reporting tools and mechanisms. These resources supported the organization's effective use of BI resources, enabling stakeholders to seamlessly access and interpret server data for strategic planning and issue resolution.
Cross-functional Collaboration for BI Project Alignment: Facilitated collaboration between IT, data teams, and business units to ensure that BI projects, particularly those involving CSP data, were perfectly aligned with the organization’s technology infrastructure and strategic data objectives. This alignment was crucial in consolidating server data into a single reporting mechanism that informed both current status and future strategic directions.
Implementation of Performance Tracking for Client Support Teams: Designed and implemented a performance tracking system for client support teams, leveraging CSP data to monitor and evaluate response times and issue resolution for high-priority clients. This system was instrumental in identifying potential problems before escalation, ensuring high standards of client service and support
Awarded Gem award for the month of February (2017) for the highest quality score in McKinsey Trivandrum
Rewarded with promotion to grade 2 within 10 months of hiring
Completed a Certification course on Tableau10 from Udemy.
Certification of Appreciation from Government of Kerala for clean-up operations at the Sabarimala temple and Pamba river
Web application for Market basket analysis and sales prediction accepted by Bagetid.dk (the online arm of Dr. Oetkers) as a solution for increasing theor online sales
Additional Information
I have worked as a freelancer for many individual clients requiring services in analytics and visualizations through various applications such as fiver and freelancer.
Academic and Capstone Projects
Sentiment Analysis of Airbnb User reviews using RapidMiner
Objective: To find the general sentiments of customers regarding each property and give them a sentiment score based on the relative sentiments of satisfaction or dissatisfaction in comparison to each other
Abstract: From the more than 16,000 properties in the database I have filtered the ones with user reviews with comments, these were then run through the sentiment analysis operator provided by Aylien and the scores have then been averaged out for each of the properties and the final sentiment scores have been given.
Tools and Skills: RapidMiner; Text analysis, descriptive analysis and sentiment analysis
Results: Created a model which when cross-verified with the ratings showed a high level of prediction accuracy
Market Basket Analysis and Sales Forecasting for Bagetid.dk (online presence of Dr.Oetker)
Objective: To find the products that were frequently bought with one another and the products that drove the sales of other products (Locomotives and Wagons)
Abstract: Bagetid.dk provided me with the sales data from June of 2018 till June 2020 of all their listed products from these I have converted them to orders and then found the frequently bought pairs using apriori algorithm and then used fbprophet to predict the sales of individual products. I have then made a web application using Streamlit to allow the marketing manager to automatically find the frequently bought pairs for each product and the predicted sales ( with all prediction variables changeable based on interest)
Tools and Skills: Python; Streamlit, FBprophet, apriori algorithm, market basket analysis and time series forecasting
Results: The final solution was greatly appreciated and put into use by bagetid.dk
Deep Learning for Image classification using TensorFlow
Objective: To build an image classification model using TensorFlow
Abstract: The given dataset had more than 40,000 images of vehicles with 10 possible types of vehicle and with a classification id to show that they belong to a specific class. I have used the multiple layers and classification networks to classify the vehicles with a high level of accuracy
Tools and Skills: Python; TensorFlow, deep learning and image classification
Results: The model was able to predict with an accuracy higher than 90% for even unclassified images and external images of vehicles
Choosing preferable mode of transport by Employees
Objective: To build a model for deciding on the mode of transport that the employees prefer while
commuting to office
Abstract: For this, multiple models such as KNN, Naive Bayes, and Logistic Regression have been
created and explored to check their model performance metrics. Bagging and boosting modelling
procedures have also been applied to create the models and model predicted mode of transport was
provided with justifications
Tools and Skills: Python; Bagging and Boosting, KNN, Naive Bayes, Logistic Regression
Results: A prediction model of higher than 90% accuracy and kappa with validation
Project Title: Building a supervised Model to cross-sell personal loans
Objective: To build a model using a Supervised learning technique to figure out profitable segments
to target for cross-selling personal loans
Abstract: A Pilot campaign data of 20000 customers was used which included several demographic
and behavioral variables. The Model was further validated and a deployment strategy was
recommended
Tools and Skills: Random Forest, Data Mining, Pruning, Model Performance Measures
Results: A prediction model of higher than 90% accuracy and kappa with validation
Tools and Skills: Python, Text analysis, descriptive analysis and sentiment analysis