Data Analyst

Tanvi Raddi

Cell: +1-469-***-**** Email: **********@*****.*** LinkedIn: https://www.linkedin.com/in/tanvi-raddi/ CAREER OBJECTIVE

Highly motivated & diligent young graduate competent in Machine Learning, Statistical Analysis aiming to build trusted models that accelerate business insights. I am eager to learn analytical thinker who is looking to collaborate in cross functional teams, leverage my strengths to resolve realistic data problems & to develop strong innovative business strategies that enhance business growth and customer engagement.

EDUCATION

The University of Texas at Dallas August 2018- May 2020 M.S., Information Technology and Management GPA 3.9 Awarded with High Distinction Honors

Savitribai Phule Pune University August 2013-May 2017 B.S., Computer Science GPA 3.7

TECHNICAL SKILLS

Languages: R, Python, SAS, C, C++, Java, HTML5, CSS3, jQuery, PHP, Bootstrap, JavaScript Database: MySQL, Oracle, MongoDB, MS Access, MS SQL, SQL Developer Tools: Tableau, SAP BI, SAP Logon, SAP Predictive Analytics, SAP Crystal Reports, Hive, Hadoop, Stata, Spark, Flume, Pig, Excel Analytical Skills: Machine Learning, Statistical Analysis, Big Data Analysis, Time Series Analysis, Predictive Analysis, ETL WORK EXPERIENCE

Data Analyst Intern - Texas Department of Insurance, Austin, Texas (R, Tableau, SQL) January 2020 – April 2020

• Developed dashboards and ad hoc reports in Tableau to identify suspicious insurance segments by analyzing their market behavior and integrating customer complaints, enforcement cases, historical fraud cases.

• Identified and reconciled errors in data storage architecture to ensure accurate business data storage.

• Identified and tracked the key indicators by running SQL queries on SIRCON database.

• Collaborated with subject matter experts to identify opportunities for operational improvements. Data Analyst Intern - Principal Financial Group, Des Moines, Iowa May 2019 – August 2019 Fraud Analysis of Retirement plan (R, K-means and Hierarchical Clustering, Principal Component Analysis, Statistical Analysis)

• Applied cluster analysis on the 401k retirement policies, analyzed the existing reports and trends of the found fraud cases to identify clusters with similar transaction patterns.

• Designed a data recommendation model that highlighted and reported suspicious transactions to improve the operational efficiency of prediction by solving the problem of imbalanced data. Cross-selling scoring framework (Penalized logistic regression, Decision tree, Random forest, SVM, Neural Networks, K folds)

• Collaborated in developing a dynamic machine learning algorithm that analyzed the historical customer data and predicted the potential customers.

Machine Learning Annotation Tool (Neural Networks, R shiny)

• Assisted in designing a machine learning annotation app to enhance data collection and training for quality inspection models in order to improve the call center’s compliance issues. Web Developer Intern – SourceKode Technologies, India (JavaScript, Bootstrap, SQL) January 2018 – May 2018

• Migrated a global trade website that significantly improved the visibility, user experience and functionality.

• Conducted research into client's product base; collated and analyzed data to meet the client’s specifications and requirements regarding accessibility, design and content. ACADEMIC PROJECTS (Python, R, NumPy, Pandas, Scikit, ggplot2, matplotlib, plotly, Tensorflow) Firearms Law Enforcement Statistical Analysis (UT Dallas) November 2019 – December 2019

• Analyzed the impact of firearms law enforcement on crime rate using entity and time fixed effects regression models to resolve the problem of data endogeneity. Recommended efficient instrument variables to ameliorate the prediction performance.

Fraudulent Firm Predictive Analysis (UT Dallas) March 2019 – May 2019

• Analyzed the firm report of an external audit company and proposed the best model from various regression, classification and ensemble learning models for improving the quality of audit work. Analysis of iOS app – Rovio Entertainment (UT Dallas) October 2018-December 2018

• Performed regression, hypothesis testing and analyzed the statistical data to determine the significant features impacting the number of installations. Presented business recommendations to boost the installation count.

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