About Dhruv Dangwal

  • Are you open to working remotely? Yes
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    United States - California, Washington, New York, New Jersey
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About me

• Experienced professional with 3+ years of experience in field of Data Engineering, Analytics and Modelling with diverse organizations
• Managed ownership of cross team analytical tools used by finance & fraud detection teams to drive incremental growth risk strategies
• Currently a Gold Medallist & pursuing Master’s in Data Science at State University of New York, Buffalo (graduation: December 2022)
• Nominated Graduate Student Ambassador and Teaching Assistant for School of Engineering and Applied Science, University at Buffalo

Skills:

Domain knowledge – Product Analytics, A/B Testing, Root Cause Analysis, Extract Transform Load, Fraud Modelling, Machine Learning
Supervised Learning – Linear Regression, Logistic Regression, Support Vector Machines, Random Forest, XG-Boost & Neural Networks
Unsupervised Learning – Principal Component Analysis, Clustering (K-Nearest Neighbors, K-Means, Hierarchical clustering)
Big Data – Hadoop HDFS, Tez, Hive, Spark, Microsoft BI Stack (SSAS, SSIS, SSMS), SQL, Teradata, Jethro, Sqoop, batch processing
Python – Data Structures, Pandas, Numpy, BeautifulSoup, Scrapy, NLTK, REST API, Matplotlib, Seaborn, ScikitLearn,
Data Visualization Tools – Microsoft Excel (pivot tables & charts), Tableau (Desktop & Server), Power BI, AWS QuickSight
Cloud Computing/Analytics – Amazon Web Services (S3, Athena, RDS, DynamoDB), Google Cloud Platform (BigQuery)
Other Tools/Skills – R, MATLAB, Shell, Git, JIRA, HTML, CSS, JavaScript, MySQL, JSON, Crontab, Jethro, PostgreSQL

Education

  • 2021 - 2022
    University at Buffalo, The State University of New York

    Masters in Data Science (STEM)

    GPA: 4.0/4.0 (Gold Medallist) Courses: Introduction to Probability Theory | Numerical Mathematics for Computing | Statistical Learning and Data Mining | Programming & Database Fundamentals | Introduction to Machine Learning | Data Intensive Computing

  • 2014 - 2018
    Netaji Subhas Institute of Technology, New Delhi, India

    Bachelors in Engineering – Electronics and Communication Engineering

    CGPA: First Division

Experience

  • 2022 - 2022
    PayPal Inc. (Internship) | San Jose, California - United States of America

    Decision Scientist Intern – Consumer Fraud Risk Strategy

    • Leveraged Click Stream Data to perform behavioral analysis during new customer onboarding to detect large scale real-time bot attacks
    • Processed huge volumes of Risk Instrumentation data (~250Million sessions/day) to research user interactions using Big Query processing
    • Performed Exploratory Data Analysis to engineer and extract features with high differential power out of 50+ unique factors and variables
    • Implemented a supervised learning model to predict 2x more carding attacks saving millions of dollars in transactional operation costs
    • Collaborated with various Risk, Instrumentation and Data Science teams to enable production ready edge variables for building strategies

  • 2020 - 2021
    American Express (Full time) | Gurugram, Haryana - India

    Assistant Manager – Product Development, International Lending Analytics

    • Developed robust automated data pipeline equipped with quality checks through Shell and Hive scripting to reduce manual efforts by 100%
    • Launched innovative opportunity sizing tool for 150+ international markets to track portfolio performance and actionable growth insights
    • Carried out profitability analysis for Customer Offer Bundling using KNN and A/B testing to study user behavior for similar groups
    • Designed Net Credit Margin & Growth Decomposition tool to ramp-up revenue margin visibility across 50+ credit product offerings Analyst – Product Development, Credit and Fraud Risk Capabilities June 2018 – July 2020
    • Delivered hybrid big data MIS reports for tracking credit and risk performance across various business verticals via Microsoft BI Stack
    • Formulated a one-stop Portfolio Pandemic monitoring for analyzing & mitigating overall loss exposure across client/merchant relationships
    • Leveraged inhouse Anomaly Detection Framework to get proactive data alerts & insights to minimize data quality issues by 30% annually

Skills