Ronit Gandhi
Data-Driven Solutions
Building intelligent systems and transforming raw data into actionable insights through machine learning and data science.
About Me
A journey from electronics engineering to data science, powered by curiosity
I'm a Data Scientist and Machine Learning Enthusiast based in New Jersey, specializing in building intelligent systems and data-driven solutions. I thrive on solving challenging problems and transforming raw data into actionable insights.
My goal is to design solutions that are not only robust and efficient but also user-focused and intuitive. Whether it's developing predictive models, optimizing workflows, or creating interactive visualizations, I bring a unique blend of technical expertise and creativity to every project.
Masters of Science in Statistics
Rutgers University - Data Science
2023 — 2025 (GPA: 3.8/4.0)
Specialized in machine learning, statistical inference, and predictive modeling
Bachelors of Technology
University of Mumbai - Electronics Engineering
2019 — 2023 (CGPA: 8.26/10.0)
Foundation in systems thinking, problem-solving, and technical implementation
Published Research
Control Engineering Practice (March 2025)
Machine Learning Model-Based Design and Model Predictive Control of a BioreactorSkills & Expertise
A comprehensive toolkit for turning data into actionable insights
Python
90%Programming
Java
75%Programming
R
85%Programming
SQL/MySQL
88%Programming
MATLAB
80%Programming
Scikit-learn
90%ML/AI
TensorFlow
80%ML/AI
PyTorch
75%ML/AI
HuggingFace Transformers
80%ML/AI
XGBoost
85%ML/AI
Random Forest
90%ML/AI
Neural Networks
85%ML/AI
EconML
75%ML/AI
Statistical Analysis
90%Analysis
Causal Inference
80%Analysis
A/B Testing
85%Analysis
Time Series Analysis
80%Analysis
Matplotlib
88%Visualization
Seaborn
85%Visualization
Plotly
80%Visualization
LookerStudio
75%Visualization
AWS
70%Cloud
Google Cloud Platform
75%Cloud
BigQuery
80%Cloud
Cloud Functions
75%Cloud
Git
88%Tools
Docker
75%Tools
Jupyter Notebooks
90%Tools
Selenium
80%Tools
Web Scraping
85%Tools
Pandas
92%Tools
NumPy
90%Tools
Featured Projects
Real-world applications of machine learning and data science
FortuneFlow
A company that allows young adults and earners to learn about personal finance and invest in the future, through proper financial planning done by professionals and state of the art AI models.
Rossmann Retail Sales Forecasting
Accurate Retail Sales Forecasting using Machine Learning, Causal Impact Analysis, and Explainable AI (SHAP). Full end-to-end pipeline for predicting Rossmann daily sales and quantifying promotion effects.
Job Postings Insights Extraction Using NLP and ML
Extraction of structured insights (skills, salary, remote status) from real-world job postings using NLP, ML embeddings, and clustering. Visualized trends across 120,000+ jobs. Hybrid LLM + NLP pipeline.
Multi-BIONER
Multi-Task Learning for Biomedical Named Entity Recognition (BioNER) with Cross-Sharing Knowledge
Gold Trading Strategies
This project explores and evaluates three gold trading strategies using Pytrends data, technical indicators (RSI, MACD, Bollinger Bands, SMA/EMA), and hybrid approaches.
NLP Stock Analysis
This project uses natural language processing (NLP) techniques to analyze stock data and identify patterns, includes a published website
Deep Learning for Image Classification
This project uses deep learning to classify land images into different categories. It evaluates 3 different DL models and draws a comparison between them
Credit Card Fraud Detection
Developed and optimized machine learning models using SMOTE, Random Forest, and XGBoost to address class imbalance in fraud detection, achieving high precision and recall through feature engineering and data preprocessing.
DTH System
Implementing a Distributed Hash Table (DHT) System for Scalable and Fault-Tolerant Key-Value Storage involves creating a decentralized infrastructure where nodes collectively manage key-value pairs.
Publications
Research contributions in machine learning and process control
Machine Learning Model-Based Design and Model Predictive Control of a Bioreactor for the Improved Production of Mammalian Cell-Based Bio-Therapeutics
This publication presents a novel approach leveraging machine learning models and model predictive control to optimize bioreactor performance, improving the production efficiency of mammalian cell-based bio-therapeutics. It highlights advanced predictive techniques and real-world validation for bioprocess engineering.
Heuristic Approach of Over-Sampling and Under-Sampling in Fraud Detection
This publication presents a heuristic approach to over-sampling and under-sampling in fraud detection. It highlights the importance of using different sampling techniques to improve the performance of fraud detection models.
Experience
Building data-driven solutions across industries
Software Engineer
- •Engineered scalable REST microservices using Python, Django, and PostgreSQL/pgvector for low-latency medical imaging retrieval and intelligent search.
- •Integrated and fine-tuned LLM endpoints (AWS Bedrock: Titan, Nova Pro, Cohere) to support RAG, generative text-to-SQL, and semantic search across clinical data systems.
- •Deployed end-to-end AI workflows on AWS (EC2, Lambda, S3, RDS) with containerized services, CI/CD pipelines, and observability dashboards for latency, drift, and system health.
- •Collaborated cross-functionally with product and ML teams to design secure, reliable backend components aligned with HIPAA-compliant system requirements.
Machine Learning Engineer - Research
- •Developed and deployed predictive models, including Random Forest, GLM, and neural networks, using Python and MATLAB, for pharmaceutical, agrochemical, petrochemical, and energy processes.
- •Integrated artificial neural networks with MATLAB simulations to optimize control strategies and enhance process efficiency.
- •Conducted experimental validation to ensure model accuracy and reliability in real-world applications.
- •Contributed to a published study showcasing data-driven solutions for complex bioprocess engineering challenges.
Data Analysis - Research
- •Developed and deployed machine learning models, including causal forests, using Python, R, Scikit-learn, MySQL and EconML to analyze the impact of robot umpires on player performance, uncovering strategic adjustments and mitigating human bias in decision-making.
Data Science Intern
- •Designed and developed a business intelligence tool leveraging Python, Google Cloud (BigQuery, Cloud Functions), and LookerStudio, increasing data processing efficiency by 40% and enabling data-driven decision-making.
- •Led a team of five engineers to manage and optimize over 40 Python scripts and cloud functions, streamlining workflows and accelerating project delivery timelines by 30%.
- •Automated competitor data extraction through advanced web scraping techniques with Selenium, enhancing market insights and improving competitive strategy formulation.
- •Optimized query runtimes by 75% and improved system responsiveness by integrating BigQuery for large-scale data analysis and operational efficiency.
Get In Touch
Let's discuss how data science can drive your business forward
Connect With Me
Open to Opportunities
I'm currently available for freelance projects and consulting work in data science and machine learning.