About
Highly analytical Data Scientist with 1+ years of experience in developing and deploying advanced Machine Learning and AI models, specializing in fraud detection, anomaly detection, and recommendation systems. Proven expertise in Python, deep learning frameworks, statistical modeling, A/B testing, and end-to-end MLOps. Adept at leveraging statistical analysis, feature engineering, and model optimization to translate complex ML solutions into tangible business value and drive data-driven strategies.
Work
Cheqlt
|Data Scientist
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Summary
As a Data Scientist at Cheqlt, I focus on developing and deploying advanced machine learning solutions to combat counterfeiting and enhance operational efficiency across supply chain and logistics domains.
Highlights
Engineered an XGBoost-based counterfeit detection system leveraging supply chain and geospatial data, increasing fraud identification by 35%.
Developed a real-time anomaly detection engine with LSTM autoencoders for logistics data, enhancing supply chain visibility by 40%.
Created a CNN-based image recognition system for product verification from user photos, boosting automated verification accuracy by 15%.
Applied DBSCAN clustering to identify geographic counterfeit hotspots from scan data, improving enforcement action success by 25%.
Performed large-scale feature engineering and hyperparameter tuning, optimizing model performance and reducing prediction latency by 20%.
Implemented SHAP and LIME for model interpretability, effectively communicating complex predictions to non-technical stakeholders and informing data-driven strategies.
Education
IIIT Bangalore
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Executive Diploma
Machine Learning & Artificial Intelligence
BMS College of Engineering
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B.E.
Civil Engineering
Grade: 8.02/10.0
Skills
Programming Languages & Frameworks
Python, FastAPI, Flask.
AI & Machine Learning
Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), PyTorch, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), XGBoost, Random Forest, Decision Tree, LSTM Autoencoders, Convolutional Neural Networks (CNN).
NLP Libraries
Spacy, NLTK.
Data Analysis & Databases
MySQL, MongoDB, Advanced SQL, Pandas, NumPy, Tableau.
Model Deployment & MLOps
MLOps, Streamlit, Docker, Git/GitHub, API Development.
Cloud Platforms & Collaboration
Google Cloud Platform (GCP), Amazon Web Services (AWS), Google Colab.
Statistical Analysis & Feature Engineering
Statistical Modeling, A/B Testing, Feature Engineering, Hyperparameter Tuning, DBSCAN Clustering, ANOVA/Chi2, VIF, KNNImputer.
Model Interpretability
SHAP, LIME.