Possible rewritten summary: As a Data Engineer at JPMorgan Chase, I orchestrate all aspects of data acquisition, collection, cleaning, modeling, validation, and visualization to deliver impactful data science solutions. I utilize Pandas and Snowflake to ensure data quality, consistency, and integrity, and employ predictive models, such as Logistic Regression, SVM, Gradient Boosting, and Random Forest, to predict patient readmission probabilities. I have also successfully implemented AWS Parallel Cluster with AWS Directory Services, reducing manual effort by 90%. With a Master of Science in Data Analytics from Northeastern University, I have a strong foundation in data analytics, data mining, and machine learning. I have led the implementation of a Time Series Price Prediction Model for 1352 Crop markets, achieving 71% accuracy in price prediction, and integrated 12 years of web-scraped weather data into the model, resulting in a 7% increase in accuracy. I have also developed and selected the deep and wide neural network (PECAD) model, and presented the results and findings to the CEO. I am passionate about transforming raw data into valuable insights, and innovating with big data analytics. I thrive on solving complex data challenges, thinking outside the box, and making a meaningful impact. I excel in collaborating with cross-functional teams, communicating effectively with both technical and non-technical stakeholders, and working in a fast-paced, collaborative environment. I am always eager to learn new skills, explore new domains, and expand my network. Let's connect and explore how we can leverage data to achieve your goals.