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Data Software Engineer

San Jose, California, United States
February 07, 2018

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Dhara Tamhane Phone: 408-***-**** Email:

Santa Clara, CA

LinkedIn Github


MS in Software Engineering Santa Clara University

Santa Clara, CA September 2015 – June 2017

BS in Computer Science Ganpat University

Gujarat, India August 2004 – May 2008

Work Experience

Software Developer (Contract) A1 Software group (Selly Automotive)

San Francisco October 2017 - January 2018

Design and develop components of business access layer and data access layer and integrate these layers to store or retrieve specific information

Based on requirements, develop applications built on top of entity framework 7.1

Importing historic data of companies, clients and prospects in new CRM

Communicating with customer support representatives in order to debug and resolve bugs in system

Documenting and performing unit testing of developed features

Tools & Technologies: Asp .Net C#, Entity framework, SQL server, HTML5, CSS3, JavaScript, SoapUI, Microsoft Azure

Summer Intern Gujarat State Petroleum Corporation Gandhinagar, India June 2010 – July 2010

•Data-driven statistical assessment of structured and unstructured data of Key performance indicators.

•Suggested recommendation in order to Increase efficacy and effectiveness of existing performance appraisal system.

•Tools & Technologies: MS access, VB.Net, MS Excel

Software Engineer Intern Bhaskaracharya Institute for Space Application and Geo-informatics

Gandhinagar, India Jan 2008 – June 2008

●Developed web application based on geographical information system.

●Implemented features for map based application to ease process of analyzing images received from satellite.

●Implemented functionalities such as map query Builder, multilayer, panning, unique color which made process of understanding complex data easier.

●Tools & Technologies: C#, HTML, JavaScript, CSS, XML, GML, Shape files


Programming Languages: Java, C#, HTML5, CSS3, JavaScript

Tools & Framework: MATLAB, Ionic, .Net, Google App engine/ Google cloud, Microsoft Azure

Database: MySQL, SQL server

Others: XML, JIRA, Agile/Scrum


Graduate Capstone Project - Github

●Developed a cross platform mobile app with a goal to provide information about various sports/gym facilities like wait times (real time/future), directions within campus etc.

●Worked on all phases of software development life cycle.

●Tools & Technologies: Java, Ionic Framework, HTML5, CSS3, Angular JavaScript, Karma, Jasmine, Jira, Maven, Google App Engine, Google Data Store

Campus Smart Cafe - Github

●Developed Java system application (GUI) that provides user a secure platform for ordering food from campus café or vending machine according to his/her set preference. Incorporated map and graph features to ensure user friendly experience.

●Tools & Technologies: Java, Swing, MySQL

Model For Organizing Threads - Github

Developed distinct thread models in Java applicable in given scenario.

Implemented multi-threading, inter process communication and increased time efficiency.

Tools & Technologies: Java, Operating system

Prediction Model for Housing Price - Github

●Developed prediction model for house price, training dataset through Gradient Descent.

●Scaled the projection for different learning rate and timeframe required for the convergence for each learning rate.

●Tools & Technologies: Machine Learning, MATLAB, Supervised Learning

Dataset Classification (Supervised Learning)- - Github

Classified test data of Iris flower (Anderson’s Iris dataset) introduced by Ronald Fisher in 1936 using Minimum Risk Bayes Decision Theoretic classifier.

Developed model from training data and classified test data accurately (99.83%) from the data.

Tools & Technologies: Machine Learning, MATLAB, Supervised Learning

Dataset Classification (Unsupervised Learning)- Github

Classified test data of Iris flower (Anderson’s Iris dataset) introduced by Ronald Fisher in 1936 using K-means algorithm.

Divide given data in K number of groups and verify accuracy of applied algorithm

Tools & Technologies: Machine Learning, MATLAB, Unsupervised Learning

Directed Research – Deep Learning

Trust Aware Recommendation System

Researched about how to generate recommendations according to users’ preferences, trust and associated groups using Modified matrix factorization algorithm with multilayer architecture by incorporating deep auto-encoder (artificial neural network). Recommends according preference, social trust and associated community.

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