Smit Chandarana

857-***-**** adg6x1@r.postjobfree.com linkedin.com/in/smit-chandarana github.com/smitchandarana Education

Northeastern University, Boston, MA Sept’18 - May ‘20 Master of Science in Computer System Engineering (Internet of Things) Courses: Machine Learning, Data Warehouse & BI, Data Structures, Algorithms, OOP (Java), Database Management and Design, ML in Finance

Gujarat Technological University, Ahmedabad, Gujarat, India Aug ’14 - Jun ‘18 Bachelor of Engineering, Mechanical Engineering

Courses: Quantitative Statics, Probability, Supply Chain, Operation Research Skills

Languages: SAS, Python (Pandas, NumPy, Scikit-Learn), Java, R, C/C++, SQL, MatLAB Machine Learning/ Regression, Classification, A/B Hypothesis Testing, Predictive Analysis, Sentiment Analysis, NLP Deep Learning: Tensorflow, Keras, PyTorch, OpenCV

Tools: MySQL Workbench, Postgress, MongoDB, Tableau, PowerBI, Seaborn, Apache (Spark, Airflow) AWS technologies: S3, EC2, IAM, RDS, EMR, Glue, CloudFormation, AWS IoT core Others: GitHub, Kubernetes, Jira, Anaconda, Jupyter Notebooks, Jenkins, BitBucket, RStudio, CI/CD, Shell Scripting Professional Experience

Data Engineer Intern Jun’20 - Present

DreamingCode, Boston

• Owned the design, development and maintenance of ongoing metrics, reports, analyses, dashboards, etc., to drive key business decisions and communicate key concepts to readers.

• Led the complete lifecycle of visual analytical applications, from development of mock ups and storyboards to the complete production- ready application using Tableau.

• Interfaced with data miners and analysts to extract, transform and load data from a wide variety of data sources using SQL and Python.

• Fine-tuned and improved query performance using profiling tools and SQL.

• Built database cubes and design star schemas using MS Visio. Machine Learning - Research Assistant

Northeastern University – GeneSys Lab April’19 – Sep’19

• Created ETL pipeline for data collection, cleaning and visualization of data from sensors.

• Analysed research papers for its best fit models, presented and defended the finding of the same to fellow peers and PhD scholars.

• Built Tensorflow based deep learning model with 84% accuracy for predication of laptop location on the wireless charging coil. Reporting and Insight Analyst

Softvan, Gujarat, India Sept’17 – May’18

• Created visualization dashboards on Tableau for more than a dozen online reports helping clients identify opportunities for more ads.

• Worked closely with the UX group to upgrade and enhance our platform and the user experience by creating new interfaces and infographics depicting complex data sets.

• Led the development of visual analytical applications from wireframe and storyboards to complete production-ready applications.

• Developed rich interactive graphics and data visualizations of large structured data in browser-friendly formats. Academic Projects

Cloud computing application using AWS and Shell scripting (CI/CD, Shell scripting, CircleCI, AWS) Jan’20 - May’20

• Developed a basic authentication enabled spring boot application for library management system.

• Automated the creation of AWS resources using shell scripts and restricted access using IAM roles and policies.

• Implemented CI/CD Pipeline using git forking workflow, CircleCI and AWS Code Deploy. FinTech Hiring Pattern (EDA, BeautifulSoup, Numpy, Scipy, Pandas, Matplotlib, Tableau, ETL) Jan ‘19 - Feb ‘19

• Collaborated with 6 teams to gather, clean and summarize the reports about FinTech and created list of 100 keywords using TF-IDF and Text rank, Numpy and Pandas and created CSV files for the same using data wrangling.

• Visualized outcomes like key areas, skills, hiring trends based on location and job sectors using Tableau and Seaborn. Prediction of Interest Rate (Linear Regression, Neural Network, AutoML, Pandas, Numpy, Tableau) Feb ‘19 - Mar ‘19

• Prepared data by extensive EDA, data cleansing and preprocessing, applied Multiple Imputations by Chained Equations (MICE) to fill missing values, used LassoCV Regularization for feature selection in Python environment with 56 different variables.

• Built models like Multiple Linear Regression, Random Forest and Neural Networks to predict lending club interest rates and summarized their respective mean absolute percentage errors (MAPE), RMSE and MAE, for both training and test data.

• Analysed data with Tableau for various findings, results gained out of project and presented to an audience of 50 students. Related Training

• Tableau for Data Science by Tableau • AWS cloud practitioner by Udemy

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