Machine Learning Engineer, Software/Data Engineer

Radhika Fichadia

+1-551-***-**** ****************@*****.*** linkedin.com/in/radhikafichadia

data mining non-relational databases cloud computing communication skills algorithm design mathematics distributed storage customer service managing ambiguity cross-functionally CloudFormation caching cloud infrastructure operating systems computer architecture deep learning problem solving TECHNICAL SKILLS

Languages : Python, SQL, Java, C#, HTML, CSS, JavaScript, Swift, Dart, Kotlin, C, C++, Go, Typescript, CPP, APEX, .Net, R Databases : Firestore, MongoDB, MySQL, SQL Server, DynamoDB, Oracle, Snowflake Cloud Platforms : AWS (S3, DynamoDB, Lambda, Cognito, API Gateway, CloudWatch, SNS, SQS, EMR, EC2), Firebase, Google Cloud Platform (GCP), Azure, Databricks

Tools : AWS-CDK, Unity, Figma, Fiori, Jira, Jenkins, Postman, Git, SRE, DevOps, Hashcat, Kubernetes, Docker, Tableau, Apache, PowerBI

Frameworks : ReactJS, SpringBoot, Flutter, Angular, Bootstrap, Redux, REST APIs, NodeJS, Django, Fast API, LangChain, TensorFlow, PyTorch, MosaicML

Agile, SDLC, CI/CD, data architecture, data analytics, microservices, web services, data engineer, XML, JSON, Excel, T-SQL, Airflow, BigQuery, UNIX, Kanban, Dataproc, apache Kafka, data structure, database, ETL, Linux, BI Tools, Spring, API, front-end, Back-end, Full-stack, customer needs, Airflow, BigQuery, product features EDUCATION

Master of Science Computer Science

Syracuse University, New York, USA

Bachelor of Engineering in Information Technology

Mumbai University, Mumbai, India

Cypress, GraphQL, Jest, end-to-end, state management, UX/UI, Refactoring, version control, AJAX, Hibernate, jQuery, data warehouse, enterprise software, code review, quality assurance, CircleCI, containerization, user interface, web performance, troubleshooting, business objectives, test-driven development, Spark EXPERIENCE

Software Developer, Internship, Syracuse University, NY May 2024 - Current Prediction of Intensive Care Unit readmission using Graph Transformers

● Developed a graph transformer model to predict ICU readmissions by leveraging the spatial relationships in Electronic Health Records (EHR) and imaging data.

● Integrated a large language model to extract meaningful representations from clinical notes, and utilized temporal transformers to capture the temporal dynamics of EHR data and medical images. J.P. Morgan Software Engineering, Virtual Internship July 2024 - Sept 2024 Analytical Software for Trading Clients

● Fixed broken files in the repository to make web application output correctly.

● Used JPMorgan Chase’s open source library called Perspective tool to generate a live graph that displays a data feed in a clear and visually appealing way for traders to monitor. Goldman Sachs Software Engineering, Virtual Internship Sept 2024 - Oct 2024 IT Security Enhancement for Corporate Governance

● Completed a job simulation as a Goldman Sachs governance analyst responsible for assessing IT security and suggesting improvements.

● Identified that the company was using an outdated password hashing algorithm by cracking passwords using Hashcat.

● Wrote a memo for my supervisor summarizing a range of proposed uplifts to increase the company’s level of password protection including extending minimum password length and using a dedicated hashing algorithm. Graduate Research Assistant, Syracuse University, NY Oct 2023 - May 2024 Hospital Readmission Prediction using Machine Learning Techniques

● Developed and evaluated machine learning models to predict hospital readmission rates, first on diabetic patients.

● Employed 10-fold cross-validation to assess the performance of each model and identified key variables impacting readmission rates, such as the number of lab procedures, number of medications, and time in hospital.

● Conducted a comparative study of five machine learning techniques: Logistic Regression (LR), Multi-Layer Perceptron

(MLP), Naïve Bayesian (NB) classifier, Decision Tree (DT), and Support Vector Machine (SVM). data visualization, data modeling, NoSQL, Dataflow, OAuth2, SSO, NLP, Natural Language Processing, predictive models, development life cycle, compliance, cloud data services, data science, user stories, project management, unit test, iOS, iPhone, team building, MVC, MVVM, Postgresql, Hive, Big Data technologies PROJECTS

ProLinkUp Kotlin, Python, Firebase, IBM Watson Machine Learning

● Designed and implemented a job/candidate matching mobile application based on the Tinder-like swiping feature.

● Used Data Analysis and shaped random forest algorithm to find potential matches reducing redundant swiping by 90%.

● Incorporated Firebase Cloud and Authentication for fast and secure data storage, analysis, and transfer.

● Arranged multiple feedback meetings with potential customers to enhance UI/UX. EZ Cloud Storage ReactJS, AWS, Typescript, Bash

● Designed and implemented a private file system application featuring ReactJS on the frontend and AWS services on the backend, managed through AWS CDK so application can be deployed on different AWS accounts.

● This system employs AWS Cognito for authentication, AWS API Gateway and Lambda for data handling/passing and CRUD operations, S3 and DynamoDB for file storage and location management, and EC2 for file name adjustments.

● The application integrates seamlessly with personal AWS accounts, ensuring enhanced privacy and customization. Smart City Waste Management System Machine Learning, Python, Java, CPP, and AWS

● Collaborated with a team of 4 to devise an efficient routing system for garbage truck drivers for waste management.

● Trained and set up an ARIMA Model for future prediction of trash collection and dynamic routing.

● Programmed microchips for sensory functions and communication with the cloud system.

● Achieved second place at paper presentation competition called Minds Eye with about a hundred participants.

● Presented paper at International Conference Multicon-W 2021. Stock Market Trend Prediction Python, SQL, NLTK(Natural Language Toolkit)

● Built a Neural Network to forecast stock price trends based on technical indicators and sentiment analysis from tweets.

● Designed a Python script for processing user tweets and extracting sentiment using NLTK.

● Enhanced model precision by 8% through feature correlation and optimizing weights in a Vanilla Neural Network.

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