Data Science Graduate along with professional expertise in Data Analysis,Cloud,Infra Ops.

Actively transitioning from SaaS Ops to a Data Science career with strong skills in Python, ML, Image Processing, NLP, LLM’s & GenAI.

Technical Skills: Data Science,Machine Learning,Tableau,R,Python,SQL,Azure,MLOPS,SRE

Education

• M.S.,Data Science

  DePaul University, Chicago, USA

• M.Tech.,Database Systems

  SRM University, Chennai, India

• B.Tech.,Computer Science

  JNT University, Hyderabad, India

Work Experience

Research Assistant@ DePaul University (Apr 2024 - Sep 2024)

• ML approach for efficient NFV Resource Management and SFC Provisioning in Fog Computing.

Lead Engineer System Support@ FIS Global (July 2019 - Aug 2023)

• Designed Power BI and Splunk Dashboards and alerts for Monitoring and automated manual tasks to boost operational efficiency.
• Executed migration of Java applications between in-house Data Centers and Cloud environments (AWS, Azure, Century Link), Achieved a 28% reduction in infrastructure maintenance costs.
• Built Mobile Banking and Switch (POS Transactions) Java Application Environments, enabling users and merchants to manage their finances 24/7 and enhancing the customer experience for major clients of FIS.

Senior Technical Associate @ Sears Holdings (Jan 2017 - March 2019)

• Migrated applications from WebLogic and WebSphere to Wild Fly (Red Hat JBoss) EAP servers, upgrading them to higher versions. This strategic shift aimed to seamlessly integrate with modern technologies, achieve superior scalability and performance, cost savings, and promote vendor neutrality.
• Implemented (CI/CD) Jenkins, Maven, Rundeck, Bitbucket, GIT, and SVN for automated deployments. This strategic integration empowered the organization to swiftly adapt to market demands.
• Implemented SSL and Vulnerability Management strategies, aimed at fostering a secure online shopping and banking experience by addressing potential vulnerabilities, and protecting sensitive information.

Clincal Database Developer @ Novartis (Jan 2015 - March 2016)

• Load and Clean Data as per the NOVDD Standards received from the CROs and Lab’s for processing data essential for clinical trials addressing unmet medical needs.
• Built clinical Oracle databases with NOVDD and OCRDC to store clinical trial data for FDA, USA, and EMEA clinical trial submissions contributing to the overall drug development process.

Projects

Sentiment-Driven Classification of Footwear Reviews of Amazon

Kaggle Publication Developed a sophisticated analysis framework that can accurately classify customer reviews into three distinct sentiment categories: good, bad, and neutral. By achieving a granular understanding of customer sentiments associated with each product, this project seeks to contribute significantly to the development of advanced recommender systems.

Forecasting Social Media Trends On Online Content through Logistic Regression

Kaggle Publication Implemented predictive analytics solutions utilizing the Online News Popularity dataset to predict article shares across social media platforms, with a focus on content published by Mashable.

Pose Recognition in images using CNN

Colab Tailored a dedicated Convolutional Neural Network (CNN) architecture specifically for yoga poses,with Keras, and clustering analysis, optimizing model performance through insights from pre-trained models and advanced feature engineering techniques.

American Healthcare: Visualizing the Digital Threat

This data originates from the U.S. Department of Health and Human Services (HHS) Office for Civil Rights portal, encompassing healthcare data breaches across various locations in the United States from Oct 2009 to Sep 2021 #Aim : To uncover patterns and insights that can help understand the prevalent vulnerabilities within the healthcare sector, particularly regarding data security. Tableau Public viz This Interactive Dashboard has been evidencing the most are the tradeoffs of highly centralized data, in our visualization there is some clear dichotomy between high tech breaches that are few and far between but very impactful, and minor breaches that make up the majority of events but don’t make the news. Whether it be on the storage end or the breaching, higher tech systems and attackers involved in breaches end up with far more victims and less frequency. Our clearest example of this is the 2015 Indiana breach of Anthem Inc. which had a very apparent sway over the data, reshaping charts as soon as it got filtered. This is seen clearly in the stacked bar charts, as their forms are completely changed without the Anthem breach. This is an outlier but it’s no coincidence that by far the largest data point was a hacking event on a network server, the two traits that tend towards the largest leakages, our visualizations with outlier free versions suggest that the measure of individuals affected is majority controlled by digital leaks whether the Anthem breach is removed or not. Another important takeaway from this report is the steady increase in breaches in recent years, although the severity of breaches doesn’t seem to be increasing but the trend of hacking in linear and other and unkown types of Breach are dropping indicating the positive sign of advancement in indentying the Potential causes and reasons behind breaches.