Professional Summary
I am an AI & Machine Learning Engineer based in the Greater New York City area, specializing in computer vision, generative AI, and large-language-model applications. My expertise includes Python, SQL, React, TensorFlow, and AWS, enabling me to develop scalable and impactful technology solutions.Key projects include:Developing a predictive dashboard from over 500,000 Medicare records, reducing analyst turnaround time by 55%.Automating ETL pipelines, significantly decreasing monthly compute costs and accelerating ML model deployment. Engineering a high-throughput streaming data stack handling thousands of messages per second with sub-200 ms latency.Technical skills: Algorithms and Data Structures (arrays, linked lists, stacks, queues, graphs, dictionaries) Efficiency optimization (time and space complexity) Cloud infrastructure (AWS, Docker/Kubernetes, Redis, SQL, Linux) SDLC, Agile methodologies, and Object-Oriented Programming. Data science pipelines (data retrieval, cleaning, training, testing). NLP, LLM integration, AI agents, and model optimization. Outside of work, my interests include music production, digital art, fashion design, fitness, cooking, photography, and graphic design. I'm eager to bring my diverse skill set and technical proficiency to new challenges.
Education
Bachelor of Science in Computer Science
Rutgers University - Newark
Graduation: May 2025
GPA: 3.8/4.0
Minor: Data Science
Relevant Coursework
Research Experience
NSF-Funded Research Assistant
2023 - 2024
- Developing novel ML algorithms for real-time data processing
- Implementing cloud-based solutions for large-scale data analysis
- Publishing research findings in peer-reviewed journals
Work Experience
Data Scientist
Fiserv | Rutgers | Berkeley Heights NJ, Newark NJ
September 2024 – May 2025
- Managed a big data warehouse with over 1 million data points across multiple databases, developing and maintain scalable ETL pipelines and optimizing data storage solutions.
- Delivered model performance updates to 3 managers, improving decision-making for services used by 50,000+ customers, by tracking key metrics and refining predictive algorithms.
- Analyzed model outputs to identify and report high-risk patterns, by leveraging Python, SQL, and machine learning techniques for anomaly detection.
Research Assistant
Rutgers | National Science Foundation | Newark, NJ
September 2023 - May 2024
- Conducted exploratory data analysis on over 500,000 patient records, by utilizing R and Python to uncover demographic trends affecting healthcare costs.
- Created visual reports to illustrate key demographic patterns, by developing dashboards and data visualizations that improved research clarity for stakeholders.
- Developed a log-linear model to predict patient costs with high accuracy, by incorporating demographic features and implementing a Count Vectorizer to handle sparse data.
- Implemented Natural Language Processing techniques to classify 10 key ICD combinations among patients, by using TF-IDF and LDA to reveal hidden medical conditions and similarities.