Research Assistant Resume

TECHNICAL SKILLS

Programming: R, Python, SQL Tools - RStudio, SPSS, RapidMiner, WEKA, MySQL, VBA

Data Analysis & Visualization: Excel, Tableau, R Markdown Web Technologies - HTML, CSS

Analytics: Machine Learning, Data Mining, Predictive Modelling, Statistics, Time series forecasting, ETL

Packages: Python • Numpy, Pandas, Matplotlib, Sklearn, Seaborn, Statsmodels • R- fpp, dplyr, lm, glm, caret

RESEARCH EXPERIENCE

Research Assistant

Confidential

Responsibilities:

Designed survey instrument to study human cognition and behavior towards data privacy in R using shiny package and hosted it in shiny server (National Science Foundation approved research)
Gatheird data pertinent to research, developed compelling visualizations in R and Tableau to gain insights
Analyzed massive and complex datasets by performing ad-hoc analysis and data management
Performed confirmatory data analysis on hypothesis stated by applying various statistical tests in R

Confidential

Jr. Executive Analyst

Responsibilities:

Developed predictive models to forecast attrition, turnover costs, performance and workforce planning
Conducted descriptive and prescriptive analytics on employee attrition data leading to 4% drop in attrition
Delivered HR Business Insights to leadership by deriving inferences from PeopleSoft HCM and Glint data

Confidential

Data Analytics Intern

Responsibilities:

Collated and preprocessed data on soil composition, types of establishments and ground water level in R
Proposed business development strategies through descriptive analytics resulting in 15% increase in revenue
Built an expert system wif RapidMiner tool which predicts water management services for areas based on their characteristics through Random Forest classifier

Confidential

Data Analyst Intern

Responsibilities:

Applied statistical techniques on turnover data and recommended action plans for retention of sales personnel
Developed Excel dashboards for employee engagement survey, human capital performance and hiring costs
Demonstrated automation of database maintenance, payroll and reporting through open source HRMIS
Performed data preprocessing in R including data scrubbing, outlier removal, missing value imputation using VIM package and variable selection using selection algorithms
Built and evaluated linear, ridge and lasso regression models wif R glm package to forecast supermarket sales