print(f{ DATA SCIENCE ENTHUSIAST })
Data Science | Machine Learning Architecture | Natural Language Processing | Large Language Models | Generative AI | MLOps | Predictive Analytics | Statistical Modeling | Time Series Forecasting
Hi, I'm Amaan Vora
and I love to code.
I am a Data Scientist with a Master's degree in Statistics (Data Science) from Rutgers University, blending technical expertise with a passion for solving complex real-world problems. My professional experience spans research engineering, development leadership, and machine learning implementation across diverse domains.
My professional journey reflects a consistent pattern of transforming analytical challenges into measurable impact. I've engineered prediction systems that significantly enhance decision-making processes, led technical teams to deliver substantial performance improvements, and developed innovative solutions that bridge sophisticated analysis with practical implementation. Each role has strengthened my ability to communicate complex insights to diverse stakeholders while driving tangible business outcomes.
Beyond technical execution, I'm deeply engaged with advancing methodologies in natural language processing and computational research. I approach each project with both statistical rigor and creative problem-solving, finding connections between seemingly disparate domains to create elegant, effective solutions.
EXPERIENCE
Lead Front-End Developer
Graduate Research Assistant (Statistics Department)
Global Business Services Intern (Machine Learning Division)
Lead Developer / Analyst
(2023 - 2024)
(2023)
(2020)
(2021)
Machine Learning Research Engineer
(2024 - present)
PROJECTS
Abstract Text Summarization Using Generative Adversarial Networks
Key Targets :
Machine Learning, Classification, Neural Networks, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
TFIDF Vectorization, BERT, RoBERTa, SiameseBERT
Programming Language :
Python
Identification and Classification of Conditions owing to Age from ICR dataset
Key Targets :
Supervised Machine Learning, Classification, Statistical Inference, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
Classification Algorithms (scikit_learn), OneHot Encoder, GradientBoost
Programming Language :
Python
Analyzing Customer Demographics and Behavior to Strategize Targeted Marketing for Online Retail
Key Targets :
Clustering, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
tidyverse, ggplot2, dplyr
Programming Language :
R Programming
Generic Recommender System for Formatted Data - Visualization via NetworkX
Key Targets :
Machine Learning, Clustering, Neural Networks, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
K-Means Clustering, Agglomerative Clustering, NetworkX
Programming Language :
Python
Analyzing and Predicting Factors Affecting Credit Approval
Key Targets :
Machine Learning, Regression, Statistical Inference, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
tidyverse, ggplot2, dplyr
Programming Language :
R Programming
Building and Optimizing a Search Application for a Vast Twitter Database
Key Targets :
Machine Learning, Database Management, Sentiment Analysis, Relational Database, Non-relational Database
Key Libraries :
PostGRESQL, MongoDB, Word2Vec
Programming Language :
Python
Analyzing Factors and Forecasting Outcome of the S&P500 Index
Key Targets :
Machine Learning, Classification, Neural Networks, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
tidyverse, ggplot2, dplyr
Programming Language :
R Programming
A Computational Linguistics Approach to Clustering Scientific Research Papers
Key Targets :
Machine Learning, Clustering, Neural Networks, Transformers, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
TFIDF Vectorization, BERT, RoBERTa, SiameseBERT, t-SNE, uMAP, K-Means Clustering
Programming Language :
Python
Analyzing Factors and Predicting the Data Science Job Market
Key Targets :
Machine Learning, Classification, Neural Networks, Data Analysis, Feature Engineering, Data Visualization
Key Libraries :
Classification Algorithms (scikit_learn), SMOTE, Tensorflow & Keras, GBM Classifier
Programming Language :
Python
