Data Scientist

VINAY KUMAR SINGH

**********@*****.*** 469-***-**** linkedin.com/in/vinaysingh08/ https://github.com/vinay99711/ EDUCATION

University of Texas at Dallas: Master of Data Science and Business Analytics Aug 2018 -May 2020 Army Institute of Technology: Bachelor of Engineering in Electronics and Telecommunication Aug 2011 -May 2015 TECHNICAL SKILLS

Programming: Java, Python, SQL, R, Tensorflow, PyTorch, SAS, PostgreSQL, MySQL, Oracle, MS SQL, Teradata Big Data: Hive, Hadoop, Apache Spark, Apache Kafka, MongoDB, Teradata, Spark ML, Spark SQL Business Intelligence: Tableau, R (ggplot2), Python (Matplotlib, Seaborn, Plotly, bokeh, interactive), MS Excel Machine Learning: Clustering, Classification, Time Series, Regression, CNN, Deep Learning and Predictive Analytics PROFESSIONAL EXPERIENCE

Advanced Analytics Intern, Hallmark Cards, Kansas City, USA May 2019 - Aug 2019

• Designed metrics and scraped ~1M tweets data for Forecasting seasonal cards sales (Python, Spark Streaming)

• Defined/Designed data pipeline and data integration to collect, clean and store large scale, cross functional dataset

• Applied advanced Statistical techniques and Mathematical analysis to understand Seasonal card sales Time Series

• Performed ARIMA modeling development, validation, implementation and experimentation to predict card sales

• Deployed Tableau dashboard sourcing data from the data lake to detect insights and provide actionable recommendation

• Reduced the forecast error rate by 70% which lead to reduction in wastage of inventory assets and saved ~$ 1M Data Scientist, Mu Sigma

Health Care Analytics, Bangalore, India July 2016 - July 2018

• Investigated and evaluated Machine Learning and Deep Learning Algorithms on multiple Drug sales to perform Predictive modelling the Sales of drugs using RNNs, CNN, LSTM on TensorFlow using Time Series data

• Experience in working on Unstructured and Structured database to perform ETL operation of Data integration and migration

• Instrumental in researching, prototyping, designing, implementing and evaluating machine learning models using Prescriber Sales rep feedback data for Multi label Classification of the performance of Sales Representative

• Digging deep into the millions of data records using distributed computing (Hive/Hadoop) and performing quantitative analysis using Jupyter notebooks with Pandas/NumPy/matplotlib and communicating Text Analytics using Data Visualization

• Researched prototype, built features and optimized the state-of-the-art Machine Learning and Deep Learning techniques like Support Vector Machine, Logistic Regression, Gradient Boosted Machine, Naïve Bayes, LSTM, CNN, RNN in TensorFlow

• Applied various transfer-learning techniques using pre-trained word-embedding like Glove, Word2vec for text similarity

• Effective targeting of Sales rep to prescriber lead to increase drug penetration by ~30% Retail Analytics, Bangalore, India June 2015 - July 2016

• Worked with online email direct marketing team to optimize cost and increase customer penetration (Hive, Apache spark)

• Designed Customer segmentation model metrics and created large database through data integration and ETL pipeline

• Demonstrated Feature Scaling, Dimensionality reduction and feature transformation using PCA and correlation plot

• Performed Exploratory Data Analysis to analyze data by doing Hypothesis testing using Statistical and mathematical models

• Used unsupervised algorithms like K-means to segment customer into cluster for effective targeting

• Provide insights and actionable recommendation in an Agile environment and identify customer to target

• Increased the Return on Investment, Customer acquisition by 40% & 14.5% and decreased customer churn rate by ~10% Research Student Aug 2018-June 2020

• Novel COVID 19 Detection Model using Computer Vision: Performed Data Augmentation technique to increase COVID 19 chest X-Ray Image dataset by a factor of 10 and developed COVID Chest X-Ray Image Classifier based on Classical Deep Transfer Learning technique to achieve an accuracy of 85%

• Real Time Tweet Sentiment of Sports Team using Kafka Streaming: Developed Real time end-to-end sentiment tracker of 10+ sports teams after scraping more than ~1 million tweets helping teams and fans understand overall public opinion

• Deep Learning Sequence Modelling for Sunspot Prediction: Design Time series model for predicting the number using Sequence Models on Classical Deep Learning framework achieving a MAE of 8.98(LSTM, GRU, Recurrent Neural Network)

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