Sales Data

HARISH DAMERA

Email: *******@****.*** Mobile: 224-***-****

EDUCATION

Master of Science in Applied Statistics Bowling Green State University CERTIFICATIONS

SAS Certified Statistical Business Analyst: Regression & Modeling. (License Number: SBARM001819v9)

SAS Certified Base Programmer. (License Number: BP050062v9)

Oracle SQL Fundamentals-I exam (1z0-051). (License Number: OC1515564)

SAS Certified Advanced Programmer (ongoing).

SAS Certified Predictive Modeler Using SAS Enterprise Miner (ongoing). TECHNICAL SKILLS

Packages: SAS, R Programming-RStudio, SAS/UNIX, SAS/Macros, SAS/SQL, SQL, SAS/STAT, SAS/GRAPH, SAS EG, SAS E-miner, SAS Visual Analytics, Python, JMP, Minitab, Tableau, Html, Microsoft Access & Excel.

Data Mining-Predictive Modeling Algorithms: Random forest, Gradient boosting, Decision tree, Logistic and Linear Regression, Generalized Additive Model, PD, LGD & EAD modeling, Weight of Evidence (WOE) and Information Value (IV), Cluster Analysis, Market Basket Analysis, Neural Networks, K-means clustering, RFM Analysis, Principal Component Analysis & Factor Analysis.

Big Data Technologies: Map Reduce, Hive and Impala. PROFESSIONAL EXPERIENCE

Graduate Assistant; Aug 2015 - Dec 2016

Bowling Green State University

Response model for Home Warranty of American Home Shield Developed a predictive model using Logistic Regression. Applied Weight of Evidence (WOE) and Information Value (IV) to handle the missing data. Missing values bin sale percent is compared with other bins sale percent and imputed accordingly. Applied Cluster analysis to reduce the number of variables in before model building to avoid dimensional complexity and over fitting.

Is it a smart move for “Change Financial Bank” to enter into Home Loans Market? Data Munging, Quality checking and Crafting a visual data narrative by Exploratory Data Analysis.

Assisting graduate students in data mining projects related to SAS-predictive modeling technologies.

Basic and Advanced SAS programming training to students at the learning Commons. Intern - Demand and Capacity Management; May 2016 – Aug 2016 LexisNexis, Reed Elsevier Technology Services, RELX Group

• Capacity planning for workloads of 8 mobile applications of LexisNexis by time series forecasting algorithms.

• Generated textual and graphical reports using SAS, UNIX platform. Automated using SAS Macros and made them accessible on the department SharePoint.

• Key Performance Indicator (KPI), Reliability Analysis based on user and system errors. Detected deviation in performance using SAS, UNIX platform.

• Predicted the traffic for the fourth quarter of 2016 for 8 applications and capacity set accordingly.

• Implemented Lagging 4hr Average instead of 75th percentile for inferences on application traffic data.

• Applied Auto Regression and Moving Average models to predict the search counts.

• Generated reports for performance monitoring. Developed weekly and monthly KPI reporting to measure customer behavior and provide actionable insights. SAS Analyst-Predictive Modeler; Apr 2015 – Aug 2015 Lodestone - Retail Analytics

Segmented the customers based on various demographics and transactional behaviors using advanced statistical methods such as K-mean Clustering. Performed z-score standardization before clustering. HARISH DAMERA

Email: *******@****.*** Mobile: 224-***-****

Generated insights from the clusters to perform target marketing based on the customer profile such as age, education, income levels and median rooms etc.

Developed visualizations to narrate the store profiles and sales.

Performed featured techniques like outlier detection and treatment, missing values treatment, variable transformation and various other data manipulation techniques.

Analyzed sales data of various stores to design a functional prototype that makes use of statistical analysis and machine learning to translate data into improved decision making, including forecasting and segmentation. Data Scientist; Statistical Data Analyst; Jun 2013 – Apr 2015 AISIN Group

• Learnt Japanese language to work effectively with Japanese teammates and communicate with teams in Japan.

Predicting the behavior of customer in purchase of an automobile by Logistic regression, Random forest and Gradient boosting algorithms using SAS Base, SAS Enterprise Guide and R.

Weight of Evidence (WOE), Information Value (IV) and Cluster analysis are applied for data manipulations.

Conducted analysis and performance reports in support of existing and new customer sales.

Predicting the sales by compiling 2009 to 2014 data considering demographics, seasonality effects and location clusters using SAS & R.

Prediction done for each month for 3 years for three different makers across different states. Handled seasonality using dummy variables.

Communicated the analytical process from data to insights to key stakeholders throughout the organization in both Japanese and English languages.

Data Analyst; Aug 2012 – Dec 2012

Eqic

Generated insights using Exploratory data analysis to interpret the change in shift causing low productivity.

Analyzed and created reports to evaluate inventory data and made recommendations.

Optimized plant fringe working hours considering profitability, while maintaining demand levels. ANALYTICS PROJECTS

Rossmann Store Sales Prediction. Source: Kaggle Problem. Tools: SAS Base, SAS E-Miner, Tableau & R. Developed a predictive model using multiple regression which helps the store managers to predict the monthly store sales. Included “Lag sales” variable in the process of predicting the next month sales. Comparison of month ahead models with complete data models. Sales dependent on previous month sales, store type, sales campaign, competition from other stores, holidays etc.

How severe is an insurance claim? - Allstate insurance Claims Severity. Source: Kaggle Problem (ongoing)

Detecting Fraudulent Transactions. Tools: R & Tableau. Identifying strange transaction reports that may indicate fraud attempts by the sales person. Exploratory Data Analysis done on unit price values of product groups. Visual data narrative by using boxplots and scatter plots.

Crash Analysis for Transportation Safety Board of Nebraska. Tools: SAS & Tableau. Identified key factors influencing vehicular accidents on 10 years of data using Random Forest, Decision Tree and Logistic Regression algorithms.

Big Data: Analysis of Climatic and Temperature Data, Source: NCDC. Tools: Cloudera Impala, Hive and Tableau.

Finding the descriptive statistics for temperatures of 40 years. Clustering is done to find weather stations with similar trends and implemented multiple regression.

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