OBJECTIVE

Data Scientist intern familiar with gathering, cleaning and organizing data. Strong math background and 2+ years of experience using predictive modeling, data processing, and data mining algorithms to solve challenging business problems.

EDUCATION

Northeastern University - May 2021 Boston, MA

Master of Science, Data Science. GPA 3.5

Coursework: Data Management and Processing, Algorithms, supervised and unsupervised Machine Learning, Information Retrieval, Linear Algebra and Statistics, Database Management Systems, Artificial intelligence, Data visualization

WORK EXPERIENCE

Carrollton Regional Medical Centre July 2020-dec 2020 Carrollton, TX

Data Science CO-OP

●Designed and built a data driven decision support system to improve the operational efficiencies and provide quality care to patients.

●Used predictive analytical tools to reduce inventory variance, gain more actionable insights into ordering patterns and supply utilization, also worked on QC of reports/datasets/visualizations, helping document and manage incoming analytics requests.

Kireeti Soft Technologies Ltd. April 2019-Dec 2019

Data Analyst

●Involved in the project titled “Data Analysis for Warehouse Management Using Qlik view”.

●Implemented analytical models and visualization reports using tableau, for a reputed Warehouse management organization in US, using SQL Server 2012 (OLTP), Qlik Sense and ASP.net MVC.

●Performed ETL operations to prevent later issues such as duplication or data degradation.

SKILLS

Worked in: R, C, Ruby, Python, java, Qlik Sense, SQL, MS Office, MYSQL(TSQL), ORACLE(SQL), probabilistic modeling and spatial modeling

Development Environments: IntelliJ, Eclipse, Visual Studio, jupyter, Tableau, POWER BI, git.

Libraries/Packages: Numpy, Pandas, Scikit-learn, Matplotlib, ggplot2, tidyr, tidyverse, dplyr, dbplyr

proficient in Microsoft Office, Word, Excel, Share Point, Outlook and PowerPoint

PROJECTS

Stock Market the Ripple Effect Jan 2019-April 2019

Northeastern University, Boston, MA

●Analyzed the stock market fluctuations to build a model of company’s relations based on their stock trend and used windowing technique to calculate the local mean and variance of a particular time frame and K-means to group the companies into clusters, standardization of the data set to achieve standard normally distributed data.

Recommender System- Review based September 2019- December 2019

Northeastern University, Boston, MA

●Built a recommender system using python that works based on the underlying sentiment of reviews given to the products. such that the recommendations made based on review sentiments should be like the recommendations based on the true ratings.

Contact this candidate