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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

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