RASHMI AYAS

Boston 513-***-**** adh210@r.postjobfree.com https://linkedin.com/in/rashmi-ayas/

https://github.com/RashmiAyas95

EDUCATION

Northeastern University, Boston, MA Sep 2019 – Present Candidate for Master of Science in Data Analytics Specialization in Statistical Modelling Coursework: Data Mining, Supervised Machine learning, Predictive Analytics, Probability Statistics, Visualization and Communication, Data management and Big Data, Analytics System Technology, Data Warehousing and SQL, BI and Decision Support Nitte Meenakshi Institute of Technology, Bangalore, India Aug 2013 – May 2017 Bachelor of Engineering – Computer Science

Coursework: Data Structure, Algorithms, DBMS, Operating Systems, Machine learning, Cloud Analytics, Big data TECHNICALSKILLS

Languages Python - Pandas, NumPy, Matplotlib, Seaborn, SciPy, Sci-kit learn, NLTK, R - Dplyr, tidyverse, ggplot2, mlr Relational Databases MySQL, MS SQL Server, Mongo DB, Dynamo DB, PostgreSQL, SQLite Analytics and BI Tableau, PowerBI, MS Excel, SAS, SAP, QlikSense, Rshiny Statistics Hypothesis Testing, ANOVA, Chi-Square, Data Distribution - Binomial, Bernoulli, Poisson, Exponential Machine Learning Regression, Random Forest, Classification, Neural Network, XgBoost, K-means, Naïve Bayes, Decision Theory Tools MATLAB, JIRA, Microsoft Project

PROFESSIONAL EXPERIENCE

Quality Assurance Analyst KPIT Technologies Pvt Ltd, India Jan 2018- Jun 2019

• Built an extensive database of documented test defects and procedures and accelerated testing time by 20%

• Optimized data collection procedures, created Pivot tables, and generated pivot reports using Microsoft Excel

• Executed risk analysis to minimize 30% of schedule hindrances with implementation of historical data trends

• Performed Project Audit in a change management system to enhance technical document compliance with company policies by 35%

• Monitored accuracy of release folders to ensure efficient products are delivered to 50+ customers on schedule without any slippage Business Analyst Intern Cerner, India Jan 2016 - May 2016

• Performed detailed validation on data collected using SQL queries and improved the reports

• Translated business user concepts and ideas to comprehensive business requirements and design documents

• Mitigated defects by 15% by developing metrics to determine inefficiencies and areas for improvement across systems

• Investigated and verified complex XML/JSON, FPML, DWML reports, enhancing the Trade Life Cycle by 70% ACADEMIC PROJECTS

Company Investment Prediction [Python]

• Developed and tested various hypothesis regarding merger and acquisitions companies

• Web Scraping and Selenium automation to gather data from Crunchbase

• Predicted if company would take part in a Merger or Acquisition Process using Supervised Machine Learning

• Built a recommendation system to suggest potential companies to participate in M&A activity using K-modes clustering and KNN. Diabetes-Prediction Analysis [Python]

• Performed data cleaning techniques to impute missing values with mean values after finding the correlation with other variables

• Implemented Random Forest machine learning algorithm to obtain an accuracy of 71% for diabetes prediction

• Executed Gradient Boosting techniques to obtain 74% accuracy for predicting diabetes in patients Automated Financial Advising Tool [KNIME]

• Propagated KNIME nodes to pre-process the data by eliminating stop words, punctuation, and unwanted characters

• Exercised the Latent Dirichlet Allocation method in KNIME to discover various topic terms present in the text documents

• Executed Inferential Statistics, Text Mining, and Topic Mining with a team of five members to automate financial decisions for clients Credit Card Fraud Detection [Python]

• Implemented Exploratory Data Analysis [EDA] to visualize the relationship between the target variable and dataset attributes

• Eliminated the problem of overfitting by deploying the algorithm to balance the target variable

• Executed mathematical models like Random Forest, Logistic Regression and Decision tree to train and classify the dataset

• Synthesized confusion matrix to compute the accuracy of all three models Human Resources Data Analysis [Tableau]

• Implemented Heat Map Analysis to understand Gender-Diversification in the Dental Magic Organization

• Executed state-wise Area Map for interpreting average payrate of all Dental magic employees highlighting high to low pay scales

• Deployed HR Dashboard by analyzing and visualizing the data for understanding the overall employee hire rate of the organization

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