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Data Scientist |Generative AI|LLMS| Deep Learning| Machine Learning
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Blenheim Chalcot
Data Scientist
June 13, 2026 – Present
DeepKick
April 5, 2025 – April 27, 2025
A computer vision and machine learning pipeline for football analytics using YOLO for object detection, K-Means for pixel segmentation, Optical Flow for motion tracking, and Perspective Transformation for player movement analysis. Enables player tracking, tactical insights, and performance analytics from match videos.
View ProjectAttribution-Modelling
March 19, 2025 – March 19, 2025
Attribution-Modelling — GitHub repository
View ProjectTime-Series-Analysis
December 21, 2024 – December 21, 2024
This Repo contains all the souce code of important topics of time series analysis
View ProjectEnd-To-End-MLOPs-Pneumonia-Diagonosis
January 11, 2024 – January 12, 2024
End-To-End-MLOPs-Pneumonia-Diagonosis — GitHub repository
View ProjectDeepLearning
January 9, 2024 – January 17, 2024
This repo contains import concepts of deep learning.
View ProjectOpenSource-AI_voice-cloning_and_lip_syncing
January 7, 2024 – January 7, 2024
This repository contains code and instructions on how to do voice cloning and lip-sync a video using open-source AI tools. This technology has the potential for entertainment, marketing, and communication. The repository includes a step-by-step guide on how to use tools like Whisper, Langchain, OpenAI,Coqui-TTS, and Wav2Lip.
View ProjectImportant_Datasets
December 28, 2023 – December 28, 2023
This Repo contains the Important Datasets
View ProjectEnd-To-End-MLOPs-Credit_Card_Fraud_Detection
December 27, 2023 – February 15, 2024
Utilizing the power of Random Forest, this project provides a comprehensive solution for Credit Card Fraud Detection. From data preprocessing to model deployment(Using MLFLOW, AWS, Docker), explore the complete MLOps workflow. Dive into the intricacies of feature engineering, model training, and real-time fraud predictions.
View ProjectCultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in diverse areas within data science and AI, including MLOps, computer vision, deep learning, and natural language processing. This breadth of interest suggests an adaptable and curious individual. However, the projects are all personal, and there is only one current professional experience listed with a future start date, making it difficult to assess cultural fit in a team or corporate environment. The lack of diverse team projects or contributions to open-source initiatives limits the assessment of collaborative fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a practical, hands-on approach to problem-solving and an interest in end-to-end MLOps, which suggests a focus on delivering functional solutions. However, there is no information regarding teamwork, communication style, or adaptability in a professional setting.