Business intelligence analyst

THEJES SV *** Poplar St, Jersey City, NJ, ***** (NYC Metro Area) •******************@*****.***•860-***-****

Masters in Analytics with 2years work experience in BI, analytics and data science. Available Immediately for opportunities.

EDUCATION

Master of Business Analytics & Project Management (STEM), University of Connecticut, CT-USA; GPA:3.52/4.0

Bachelor of Engineering, Anna University, Chennai-India; GPA: 7.95/10.0

PROFESSIONAL SKILLS

Tools & Languages: R, SAS, SPSS, MS SQL Server, Alteryx, Tableau, MS Power BI, Domo, Looker, Python, Google Analytics, Oracle, Access, Business Objects, JIRA, Confluence, Oracle SQL, MS Project, MS Visio, MS Excel, Google Big Query, SSRS, Cognos

Statistics Concepts: Regression, Decision tree, Random forest, Clustering, Classification, Hypothesis testing, Naïve Bayes, PCA, Discriminant Analysis, Principal Component Analysis, Boosting, Model Selection, Dimensionality Reduction

Certifications: GAIQ (Google Analytics Certification) •Udemy Certified A to Z Machine Learning with R, Python

WORK EXPERIENCE

Conduent (Xerox) Florham Park, NJ • Business Intelligence Intern Sept 2017- Present

Developing a HR Attrition model using R to predict employee attrition rate and find key actionable insights to prevent loss

Designed KPI summary dashboards using Tableau and made data driven recommendations to increase revenue by 32%

Retrieved data using SQL for tableau dashboard redesign and acted as liaison to drive data based decision making

Created Alteryx workflows and performed ETL operations to ensure data is ready for visualization in Tableau and DOMO

Johns Hopkins Medicine •Data Analyst Intern May 2017- Aug 2017

Created data life cycle documentation- data dictionary, data mappings and flows/diagrams to reduce ETL process time

Performed UAT, data validation to prevent errors and implemented agile project management using JIRA and Confluence

Extracted data using SQL, developed Tableau dashboards and provided support functionality for data warehousing team

Gathered requirements, developed use cases and created wireframes to kick start data visualization in SSRS, Tableau

Stephen's Soldiers Foundation, Data Analyst Graduate Consultant Aug 2016- May 2017

Analyzed qualitative user data using R and visualized using Tableau and excel to produce insights on events and strategies which would improve volunteer base of the organization. Performed A/B testing to suggest improvements and increase use.

Airbnb, Duke University •Data Analytics Consultant Trainee Sept 2015- Jul 2016

Built regression & classification models using R to predict short -term rental prices Optimized rents by 30% and maximized profits by 65%. (https://public.tableau.com/profile/thejessv#!/vizhome/Thejes_Business_Recommendation/Dashboard1)

Surprise Solutions •Marketing Analytics Intern May 2015- Aug 2015

Worked with Ola Cabs to increase user completion rate, find reasons for poor performance and improved traffic by 15%

Modified Webpages, tracked performance using Google Analytics, created reports, dashboards, maintained metrics for a web based product and provided recommendations from analyses to increase user activity.

Performed sentiment analysis to understand Customer behavior and improved interactivity based on the results

Noble Tech Industries • Business Intelligence Analyst June 2014- Jan 2015

Analyzed quantitative data using SQL, SPSS, R, Alteryx and identified relevant findings to improve resource utilization

Analyzed business requirements, translated them into technical requirements, developed complex SQL queries to extract data, designed Alteryx Workflows for data preparation (ETL) and designed tableau dashboards for providing insights

Performed data cleaning, merging, tabulation, exploratory data analyses, data manipulation to prepare data for modelling

Interpreted data using Statistical tools like R and provided business performance reports based on descriptive statistics

DATA ANALYTICS PROJECTS

Human Resource(HR) Analytics–Built a Churn Model using R for predicting employee attrition with Logistic, Stepwise logistic, and decision tree, Random Forest, Boosting Models and selected the best model with 92% accuracy.

Predictive Modeling Movie Rating- Followed SEMMA Approach, used classification model to predict the movie rating with accuracy of 80% and segmentation of the movie based on profit categories.

Predicting F1 Results - Predicting F1 Race wins with historical data and generated insights to take best decisions for Racers and Constructors. (https://public.tableau.com/profile/thejessv#!/vizhome/F1RacesDashboard_0/Story1)

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