Data Analyst - R Python SQL, Marketing Analyitcs
Resume:
RUSHIKESH MAHESHWARI
DATA ANALYTICS & DATA SCIENCE
CONTACT
*********.************@*****.***
Tampa, FL, USA
linkedin.com/in/rushikesh-maheshwari12/
github.com/rmaheshwari12/
PROFILE
An agile team-player and experienced data
science professional having hands-on
experience in statistical analysis, natural
language processing, and machine learning,
looking for opportunities with customer-
centric role, to benefit business by data
driven decisions
EDUCATION
UNIVERSITY OF SOUTH FLORIDA MAY 2020
Master of Science in Business Analytics and
Information Systems
UNIVERSITY OF PUNE MAY 2015
Bachelor of Engineering in Mechanical
MANAGEMENT SKILLS
Analytical problem solving
Agile project management
Communication & decision making
JIRA, Git, Excel, PowerPoint, Visio
TECHNICAL SKILLS
Statistics and Probability - bayes theorem,
hypothesis testing, central limit theorem
Data Wrangling & Machine Learning -
regression, classification, clustering,
pandas, numpy, scikit-learn, statsmodel,
WEKA, Azure ML, SAS-EMiner
Programing Languages - Python, R, C#,
HTML, Javascript
Natural Language Processing - genism,
nltk, tweepy, spaCy, topic modelling
Database & Distributes Computing -
PySpark, Databricks, Hive, Alteryx, HDFS,
SQL Server, MySQL, SSIS
Data Visualization - Tableau, Power BI,
matplotlib, plotly, seaborn, ggplot2
Cloud Computing - AWS EC2, SageMaker
Certifications
Google Analytics for Beginners
SAS-USF Analytics and Business Intelligence
INDUSTRY EXPERIENCE
Data Science Intern ConnectWise-USF Nov 2019 – April 2020 Tampa, FL
Used combination of SQL Server and PySpark to acquire 13 Million service tickets data, preprocess text summary with NLTK and custom regex expressions
Built cascaded multiclass classification models with Naïve Bayes and XGBoost to predict incoming ticket attributes like priority, resolution time, ticket movement reducing 35% cycle time
Helped product manager and data scientist to capture process bottlenecks, data quality issues and provide recommendation to adapt predictions as tool feature Graduate Assistant University of South Florida Jan 2019 - Aug 2019 Tampa, FL
Enhanced coursework for statistical data mining & data visualization and mentored 30+ graduate students for statistical modeling projects
Contribute towards faculty research projects and literature studies Business Analyst Intern Defour Analytics Pvt. Ltd. Jan 2018 - Jun 2018 India
Gathered requirements and mapped business processes to understand problem definition and pre-process required data for analysis using Python and SQL
Developed statistical models using R to suggest business process optimization and enhance KPI reporting
Procurement Analyst Zycus Infotech Pvt. Ltd. Feb 2016 – Oct 2017 India
Built UI forms, conditional approval workflow, KPI questionnaire and scorecards, to reduce on-boarding and evaluation cycle time by 40%
Created Interactive reporting dashboards and ad-hoc reports for CPOs and CFO’s to make strategic sourcing decisions
Normalized and classified customer transactional data using SQL Server and AI classification engine reducing 30% maverick spend and supplier fragmentation
Worked with product managers to enhance product usability and to build, test and validate features like one-view reporting dashboard and claims management system DATA SCIENCE PROJECTS
Eliminating racial bias in COMPAS risk assessment (Spring 20)
Used linear regression to build fair risk score in R, used for pre-trial release of criminals to eliminate racial bias towards african-american criminals
Built classification model using logistic regression to predict recidivism on risk score and features extracted from crime description in python with 78% f1 score Clustering of legal documents (Fall 19)
Created clean text corpus by pre-processing 4000 legal documents using nltk, regex, web scrapping to extract catch phrases in python
Applied K-Means Clustering using TFIDF and Word2vec forming 7 distinct clusters for legal cases which eases sorting and retrieval of documents Microfinance business expansion (Summer 19)
Used Web Scrapping with R to collect, pre-process and map demographic, financial and economic data to explain business operation of a branch
Applied Logistic Regression to predict a location’s probabilistic profitability at 86% precision to help plan and prioritize business expansion Customer segmentation (Spring 19)
Customers Segmentation based on recency, frequency, and monetary behavior for targeted marketing strategies of 1 million e-comm data
Applied unsupervised K-Means Clustering resulting in 8 customer personas using elbow method using PySpark and HDFS offering scalability
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