MS Data Analyst offering a unique synergy of Operations Research with Control Engineering, seeks a position to apply advanced data processing and analysis skills.
Programming Languages: C, C++, JAVA, Python, R, SQL, MongoDB, Scala
Software: MATLAB, Spark, Octave, Gurobi, MS Office Suite, Code Composer Studio, Arduino, Photoshop, Corel Draw
Confidential, New York
- Collaborated with a team of five to deliver an interactive app for underwriters that predicts creditworthiness of small and medium business loans by analyzing not only financial information such as submitted tax information but also geolocation, demographic profile, and other parameters.
- It is expected to reduce the percentage of unrecoverable debt by 10%.
- Developed a 17% more effective credit rating score by using principles of advanced data analytics and prediction modeling.
- Usually these risks are based on internal portfolio data, however, this innovative model pulls in proprietary external risk factors based on employment growth, median income, population density, etc. to improve its effectiveness.
- The final product used programming skills in R, Python and Tableau to create a visual interface for users.
- Created new KPI (Key Performance Indicator) dashboards for Confidential refinery using skills in electronic control communication and visual programming. These practical dashboards were used by senior engineers to make decisions on day to day operations.
- Upgraded Process and Instrumentation diagrams for client to capture a vast set of real time information from the sensors and render the information in real time graphics. Used instrumentation engineering, programming, and knowledge of ISO standards to deliver.
- Analyzed Twitter feeds in conjunction with company stock prices to a point where tweet sentiments could be used to predict market hourly trends. Tweets, by their nature, need natural language processing programming skills to be analyzed.
- Employed machine learning algorithms to auto parse real estate data, such as number of bedrooms, area, view, year constructed etc., along with crime rate to build a model that can predict housing price.