Data Scientist

Rahul Tej Kotagiri

Phone: +* *** *** (****) Email: **************@*****.***

Dallas, Texas

TECHNICAL SKILLS

Languages: Python, R, SQL, HIVE, PIG, Linux, H2o.ai, JS

Machine Learning XGB, GBM,, Ensemble models, K-NN, Decision Tree, Random Forest, K-Means clustering, Sequential Rules, SVM, Regression models,

ML Techniques Regularization, Hyper Parameter Tuning, Feature Engineering, Dimensionality Reduction.

Deep Learning(DL) OpenCV, Neural Networks, Deep Neural Networks (CNN & RNN) and LSTM,

DL Frameworks Tensorflow, Keras and Pytorch.

Data Visualizations Tableau, MicroStrategy, R, Python

Big Data Hive, PIG, Map Reduce, Hadoop

Web Applications: R-Shiny, HTML, JS

Data Science Skills: Predictive Modeling, Machine Learning, Image Processing, Analytics, Statistics, Data Sciences, Data Mining, Text Mining, NLTK, Exploratory Data Analysis, Forecasting, Optimization, Mathematics, AIOps, MLOps.

Data Skills: Structured/Unstructured Data, Images, Videos,Graph, Big Data Technology, Data Mining & Visualizations

PROFESSIONAL EXPERIENCE:

Verizon USA

June 2018 – Till Date

Data Scientist (Machine learning)

This platform is to develop machine learning models and develop integrated analytics platform called VOC for the digital web purchase lifecycle.

Responsibilities:

●Develop Machine Learning and fraud detection models to ensure smooth purchase flow in digital sales.

●Support product, sales, leadership and marketing teams with insights gained from analyzing company data.

●Analyze Images and extract financial information and credit card automatic info detection purchase workflow.

●Achieved reduction of recurring chats and calls up to 11% and 7% respectively by filtering redundant inquiries and implementing analytics workflow for Natural language processing on the customer chats/calls and design pipeline to output chat insights from large volumes of chats data (Voice of Customer) using techniques like RNN, bag of words, topic modelling etc.

L’Oréal USA

February 2017 – April-2018

Data Scientist (Python Programmer)

Gloria L’Oréal: Intelligent Machine Learning Engine for Labs – Incubator

This platform is to develop machine learning models into an integrated product platform for all the scientific and consumers can use it for product development L’Oréal landscape.

Responsibilities:

●Develop Image processing structures using OpenCV in Python, and fed into a Convolution Neural Network and machine learning models to predict the Expert(dermatologist) scores of the facial and hair features before and after L’Oréal Cosmetic application.

●Created a web platform called SPRINT- simulation for hair color and formula detection using RGB/LAB values to detect closest formula using clustering and simulations for research purposes.

●Created an Acne detection algorithm and released in app store to detect if the facial image has acne or not using opencv and CNN model using video cache.

●SWOT analysis of the different developed models and techniques of machine learning models.

●Analyze statistical inferences and predict customer and product behavioral dynamics.

●Develop Machine Learning models, validate and push to production. Prediction model based projects over different expanse of projects to meet the needs of marketing & scientific teams in L’Oréal USA.

●Involves core research and developing scalable data science products for making key decisions. Following data science CRISP-DM protocols for project executions.

●Apply statistical and econometric models on datasets to measure results and outcomes, identify causal impact, attribution and predict future performance of users or products in an agile development

Bowling Green State University, OH – Decision optimization and Simulation Lab

January 2016 – August 2016

Operations Research– Graduate Assistant

●Optimization Case Study: for Sports Analytics : https://goo.gl/GukH3x

●Negative Outcomes Risk Prediction Model: Analyzed Medicare resource utilization groups (RUG’s) and Managed Care insurance claims data from healthcare provider and predicted residents with negative margins using Regression and CART.

●Performed clustering analysis on historical patient level data to classify them into payment (total expense per stay) groups and identified parameters expenditures and provided recommendations to drive reimbursements.

Vizury – A Big Data & Data Science Ad-tech Firm (Clients: Ecommerce, Hotel and Financial clients of Asia, USA and Australia)

July 2014 – July 2015

Data Scientist

Intellibid: Programmatic Bidding Algorithm – Data Sciences: Intellibid is Real Time Bidding (RTB) Engine developed by Data Science team of Vizury. This Engine predicts the worth of an Ad Impression in Real Time & sends bid accordingly. It was built to predict Click-through-Rate (CTR) & conversion rate (CVR)

Responsibilities:

●Develop and Improve the performance of the prediction models using different regularization, hyperparameter tuning to minimize the cost function.

●Product enhancements with improved prediction metrics of the CTR models using feature engineering, outlier detection, Class Imbalance problems and missing value detection.

●Leverage analytics to optimize real time bidding strategies on major Ad-Exchanges e.g. Google, Facebook, for ad campaigns in order to maximize revenues and profit margins for the company.

●Implemented Logistic Regression, Linear Regression, Bagging, Boosting, Decision Trees, Clustering Algorithms, Support Vector Machines (SVM), optimization and Stochastic Processes to predict CTR and CVR models (Click through rates and Conversion rates) and to increase overall sales

●Analyze customer user data using big data tools Hive, Pig to understand click through patterns & buying behavior that helped increasing customer engagement & conversions for E-Commerce Clients.

●Work closely with product engineering team to propose, validate and iteratively build data-driven product features like recommendation system, Rules, Product proximity match etc.

iQuanti, Inc - Digital Consulting & Analytics to leading clients (Discover, American Express, Allstate)

June 2013 – June 2014

Data Science Analyst

Transaction Lifecycle Segmentation Analysis for Client: American Express

The project objective was to provide deep insights into Transactions and inventory data that will empower clients to make better decisions in each of the stages across the lead to order lifecycle.

Responsibilities:

●Analyze the customer behavioral patterns using classification and segmentation techniques.

●Calculate Customer Lifetime value (CLTV) and churn/attrition rates for different demographic data.

●Analyze data and identify financial trends in customer behavior, acquisition, retention, and customer movement through the website.

●Predicted the google search volume for keywords based on several factors using linear regression

●Interacted with client side Business Analysts and Technical Leads for requirement analysis and to define Business and Functional Specifications for a leading Finance Company

●Analyze structured data & identified top profile customers using Correlation & Regression Techniques.

EDUCATION

●Master of Science in Analytics(Data Science), Bowling Green State University (BGSU),Ohio 2016

●Big Data Analytics Certificate degree, Carnegie Mellon University, PA, (INSOFE), 2015

●Bachelors’ in Materials Engineering, National Institute of Technology, Warangal, India, 2013

CERTIFICATION

●Foundations for Data Science Stanford University 2016

●Big Data, Strategic Decisions, Analysis to Action Stanford University 2016

ACHIEVEMENTS:

●Gloria platform at l'Oreal overall improved time and cost reduction in survey and research hours upto $1.2 million yearly.

●Increased Vizury global Revenue from $6 to $7.2 million MoM increase by enhanced model deployment.

●Recognized for exceptional performance in JFM-2015 Quarter in Vizury.

Contact this candidate