Daksh Patel

Boston,MA 832-***-**** ad34qi@r.postjobfree.com linkedin.com/in/patel-daksh github.com/Patel-Daksh Enthusiastic graduate with 1.5 years of hands-on expertise in data science and software engineering. Proficient in Python, R, Java, and databases. Achieved a stellar 94% accuracy rate in time series predictions during a dynamic internship at CREWASIS. Ready to infuse innovation and experience into an entry-level role. Education

Northeastern University GPA : 3.63/4.00 Boston, MA Master of Science in Information Systems Jan 2022 – Apr 2024 Gujarat Technical University GPA : 8.77/10.00 Ahmedabad, Gujarat Bachelor of Engineering in Information and Communication Technology Aug 2017 – July 2021 Technical Skills

Languages: Python, R, Java, SQL, JavaScript, Scala, Dart, HTML/CSS, C++, C Databases: MongoDB, MySQL, Firebase, PostgreSQL, Microsoft SQL Server, Flask Developer Tools: Git, Docker, AWS, Power BI, Tableau, Looker, JIRA, SAS Libraries: Pandas, NumPy, SciPy, Sklearn, Matplotlib, Keras, TensorFlow, OpenCV, PyTorch, Scikit-learn, React Experience

Data Science Intern - CREWASIS - Brooklyn, NY (4 Months) Sept 2023 – Dec 2023

• Integrated ETL (Extract, Transform, Load) processes with BeautifulSoup to gather data from diverse sources like social media and news articals & Subsequently, generated pickle files from the extracted data and performed topic modeling using Latent Semantic Indexing (LSI) for data analysis on a dataset comprising over 2M data points

• Implemented time series algorithms (ARIMA, FBProphet) to predict future trends with an impressive average accuracy of 94%, enabling proactive decision-making and providing crucial business insights

• Devised sentiment classifiers for customer feedback analysis of social media data, yielding actionable recommendations through Machine Learning insights for market-based product analysis

• Collaborated with CEO and Marketing team to strategize data solutions, leveraging Tableau insights for reports and contributing to cross-functional teams for data processing and model validation Graduate Teaching Assistant - NEU - Boston, MA (4 Months) Sept 2022 – Dec 2022

• Prepared engaging presentations on advanced Java topics, such as object-oriented programming & data structures

• Communicated with the course instructor for creating course materials, including lecture slides and lab solutions

• Demonstrated best practices by problem-solving sessions resulted an avg improvement of 9% in students’ grades Software Engineer Intern - ELYSIOT - Milpitas, CA (5 Months) Aug 2021 – Dec 2021

• Collaborated with a cross-functional team of software engineers using JIRA to implement agile methodologies, incorporating data modeling concepts with Firebase for validation and encrypted cloud storage.

• Implemented and integrated 15 micro-functions for specific operations, and optimized efficiency by transforming 20 recurring Flutter components into reusable elements

• Engineered a push notification system with Firebase Cloud Messaging (FCM) and REST API, optimizing performance by implementing cached notification templates for 25% quicker delivery

• Designed dynamic Rive animation integration with Flutter, enabling visually appealing front-end animations for individual users, which boosted site traffic by 17% Data Science Intern - BIM INFRA. LLP - Ahmedabad, Gujarat (6 Months) May 2020 – Oct 2020

• Performed statistical analysis and forecasting on data from multiple IoT sensor products, resulting in improved accuracy of real-time progress monitoring for road building projects

• Enhanced predictive modeling accuracy by 8% through the implementation of Bi-Directional LSTM with Transfer Learning techniques, leveraging deep learning methodologies over previous linear regression models

• Cooperated with a team to identify critical success factors for BIM software selection, employing a combined approach of the Factor Comparison Method (FCM) and Fuzzy DEMATEL method, and published a research paper documenting the findings received more than 20 citation Projects

TradeSense AI AWS (SageMaker, Lambda, API Gateway, SNS, S3) Dec 2023 – Feb 2024

• Successfully integrated cutting-edge AI technologies, including Amazon S3, SageMaker, Lambda Functions, API Gateway, and XGBoost, to create a seamless and efficient platform for day-ahead stock price predictions

• Designed and implemented a user-friendly API, simplifying the process for end-users to make POST requests

• Engineered and implemented an email subscription service using Amazon SNS, ensuring subscribers receive timely notifications with predicted stock prices, enhancing user engagement and market awareness AIDOC Flutter, API, TensorFlow, Firebase, Git, Flask Aug 2020 – July 2021

• Innovated cross-platform application assisting patients with disease diagnosis using scanned X-rays & MRI images

• Involved actively in Front-end development, Backend Scripting, API integrations, web designing and deployment of the database using the Flask server and Flutter

• Utilized computer vision by integrating a Convolutional Neural Network (VGG-16) with TensorFlow and TensorFlow Lite, achieving an 83% accuracy rate in image recognition.