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Engineer Social Media

San Jose, CA
May 17, 2020

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Highly effective and passionate Software Engineer adept to collecting, analyzing and interpreting large data sets, developing new data regression models using predictive learning and delivering high impact data driven insights to non-technical business audience. Possessing extensive analytical skills, strong attention to detail and a significant ability to lead by decision making.




• R

• Machine learning

• Deep Learning

• Spark


• Tensorflow

• PyTorch

• MongoDB

• Node.js


• Scikit-learn

• Splunk

• Hive

• Redis

• Mamcached

• A/B Testing

• Python

• Tableau

• Statistics


• Hadoop

Autodesk - Data Science Intern

San Francisco, CA • 05/2019 - 08/2019

• Outlined key sabbatical patterns of Autodesk employees to Finance-Audit Team using Splunk's active directory data

• Developed predictive model for job retention and projected time series sabbatical trends

• Performed exploratory data analytics, data visualization and identified KPIs using SQL, Python and Tableau

• Deployed project on AWS, automated Rest API data extraction and email notification

• Presented data driven insights to C-staff and recommended resource and software licensing reduction by 15%

Infosys Limited - Senior System Engineer - Data Analyst Pune, Maharashtra • 09/2013 - 09/2016

• Evaluated overall impact of Email campaigns through A/B testing, success KPIs and experimentation

• Analyzed 70 GB data using SQL to formulate reports on performance metrics like Transaction, Revenue & Loss

• Introduced automation efficiencies in campaign reporting using Tableau that saved ~25% of resources

• Performed Sizing to understand the market size and targeting population. Smart targeting resulted an incremental lift in WORK HISTORY

San Jose State University

San Jose, CA • 12/2019

Master of Science: Software


Poornima Institute of

Engineering & Technology

Jaipur, India • 05/2012

Bachelor of Science: Information


· Bayesian Machine Learning in

Python: A/B Testing

· R Programming A-Z

· Python for Data Science and

Machine Learning Bootcamp

· Statistics for Business

Analytics A-Z

· Master the art of Dynamic


· Deep Learning Using Keras

· Master the coding Interview:

Data Structures + Algorithms

• Java

• Pandas

• Keras

• PySpark

• React.js

• PostGREsql


• Predictive Modelling

• Logistic Regression

• Cassandra

• Kafka

• Teradata

• Regression Analysis



revenue by $3/customer and activation rate by 4%

Vertax Web Technology Pvt Ltd - IPhone/ IPads Application Developer

Jaipur, Rajasthan • 12/2012 - 09/2013

• Proficiency with XCode IDE and iPhone SDK. Developed multiple IOS apps and social media websites for US based retail client

• Developed UI interface for different iPhone versions, integrated using web service APIs and have firm knowledge of Object-Oriented Programming

• Collaborated with client and internal teams to design application workflow and performed unit and QA testing 1) Smart City - Internet of things, San Jose State University

· Implemented an IoT based smart city application to track the temperature and humidity of geographic areas by clustering

· Developed front end using REACT and backend using JavaScript Node.js and performed load balancing when deploying to AWS.

· Optimized the runtime of SQL code for data manipulation and automated a Tableau dashboard to capture geospatial sensor conditions

2) Distracted Driver Detection, San Jose State University

· Developed Supervised Multiclass classifier, preprocessed features using Dimensionality Reduction and Keras library, evaluated models Support Vector Machines and CNN and performed hyperparameter tuning

· Performed GridSearchCV to optimize models, cross validated using K-fold & implemented dropout to counter overfitting 3) Heart Disease Prediction, San Jose State University

· Implemented Supervised regressor model and analyzed data to obtain train-test split, evaluated R2 to calculate coefficient, pre-processed using Pandas, Numpy, Scikit-learn and visualized using Matplotlib

· Developed models with Gaussian Naive Bayes, Decision Trees, Ensemble Methods (Bagging, Adaboost, Random Forest), K-NN and Logistic regression, demonstrated Bias-Variance tradeoff along with Precision vs recall vs F1-score


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