Abinesh Senthil Kumar

adikdj@r.postjobfree.com 716-***-**** github.com/abinesh-23 linkedin.com/in/abineshsk/ Mathematics enthusiast and Industrial Engineering graduate student with a passion in Data Science and Analytics. Curious, Proactive and Outcome-oriented engineer interested in using EDA and Machine learning algorithms to predict and examine patterns in data to deliver inferences and implement action-oriented solutions to complex problems. EDUCATION

University at Buffalo, The State University of New York. Aug 2019 - Feb 2021 Master of Science in Industrial Engineering GPA - 3.83/4.00 Graduate coursework: Statistical Data Mining, Data Analytics and Predictive Modelling, Programming and Database for Data Science, Data Models and Query Language, Design of Experiments. Anna University, India. Aug 2014 - May 2018

Bachelor of Engineering in Mechanical Engineering. PROFESSIONAL EXPERIENCE

Data Analyst Intern – iMedX Inc., Atlanta, Georgia. Jun 2020 - Present

• Recommended strategies by performing various market research activities such as Competitive analysis, Win- Loss analysis, Response modeling and worked on employee Churn prediction projects.

• Collaborated with cross-functional teams to identify data sources and pulled data using SQL and SOQL queries.

• Identified potential customers worth $25 million by developing an ensemble predictive model on opportunities data and automated the whole model training process in Python.

• Assisted marketing campaigns by performing response modeling to target customers who are likely to respond.

• Executed analysis on various sources of data including customer relationship management (CRM) data to comprehend Key Performance Indicators (KPI) by connecting to Salesforce API.

• Designed and built sales dashboards in Tableau to facilitate understanding of business statistics for C-suite.

• Presented Business Intelligence reports that provided actionable insights to support decision making process.

• Validated acceptance criteria for analytics dashboards by performing User Acceptance Testing (UAT).

• Conducted data analysis to support healthcare analytics platform (revenue cycle management). Graduate Research Assistant - University at Buffalo (SUNY). Aug 2020 - Present

• Working on a research project partnering with the American Medical Association (AMA) to assess the factors causing physician mental health issues during COVID-19 pandemic using a survey-based approach.

• Identified risk factors that contribute to PTSD, Burnout and other mental health issues by analyzing data.

• Conducted data cleaning, wrangling, visualizations and analysis of survey data collected from over 1000 physicians in USA to answer various ad-hoc research questions in R (Programming language).

• Understanding various sentiments and performing text-mining on responses from physicians. CERTIFICATIONS

• SAS Specialist: Base Programming using SAS 9.4 - Issued by SAS – Credential ID: HLWDN0RK2E41Q59C [SAS]

• Six Sigma Green Belt – Issued by IISE – Credential ID:14034978 [IISE] ACADEMIC PROJECTS

Analysis of COVID19 cases in USA - Carried out Time Series Analysis and utilized ARIMA model to forecast infection, death and recovery count. Applied boosting methods to identify feature importance and interpreted partial dependence of predictors with infection count and death rate. Sentiment analysis of twitter data - Extracted twitter data to understand polarity and subjectivity. Implemented bag-of- words model for feature extraction and used random forest to classify stock price change based on relevant tweets. Database creation for university management - Designed and built a relational database schema satisfying requirements by creating Entity-Relationship (ER) diagrams and wrote Data definition language (DDL) scripts. Performed Extract Transform Load (ETL) tasks to load data into database and queried using SQL in MySQL. Prediction of accidents and their severity - Extracted, cleaned, visualized and analyzed the traffic accident dataset with more than two million records to identify the key metrics and implemented parametric and non-parametric machine learning models for prediction of accidents. Created dashboards and visualized data in Tableau. Experimental design to get maximum score in dart game - Conducted ANOVA by designing a fractional factorial design in Minitab and used response optimizer to optimize the factor settings to attain maximum score. TECHNICAL SKILLS

Techniques - Feature engineering, Hyper-parameter tuning, Regression, Classification, Anomaly-detection, Cluster- Analysis, Principle Component Analysis, Sentiment-Analysis, Hypothesis testing, A/B testing. Languages - R (dplyr, caret, ggplot2, lubridate, R-Studio), Python (NumPy, Pandas, Scikit-Learn, Keras, Matplotlib, Seaborn, Spyder, Jupyter Notebook), SAS, SQL (MySQL, Microsoft SQL Server). Software - Tableau, Jira, Minitab, MS Excel (Pivot, Vlookup, Hlookup, Macros), MS Word, MS PowerPoint.