Machine Learning Data Scientist

Charan Kumar Pathakamuri

Baltimore, MD 410-***-**** *********@*****.*** LinkedIn GitHub

Results-driven Data Scientist with expertise in financial forecasting, API development, machine learning, and data analytics. Skilled in Python, Java, SQL, Power BI, and cloud deployment (AWS). Experienced in developing real-time financial analytics tools, predictive modeling, and scalable machine learning pipelines. Passionate about fintech, data-driven decision-making, and intuitive user experiences.

Technical Skills Programming Languages/Tools: Python, FastAPI, Chroma DB, MS SQL Server, Java, Power BI, Databricks, HTML, CSS, JS Frameworks/Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Web Scrapping, Gemini AI, Open AI.

Analytics Skills: Natural Language Processing (NLP), AI-Powered Information Retrieval, Vector Embeddings, Semantic Search, Knowledge Indexing, Model Optimization, Machine Learning, Predictive Analytics, Statistical Modeling, Data Mining, Data Visualization, Database Management, Time Series Forecasting.

Cloud & Deployment: API Development, Persistent Storage, Large Language Models (LLMs), Query Optimization, Chatbot Development

AWS: Amazon S3, Route 53, SNS, Lambda, SQS, Kinesis, VPC, EBS, EC2

Interpersonal Skills Communication Skills, Collaborative, Problem-Solving Mindset, Leadership, Public Speaking Skills, Presentation, Attention to Detail, Time Management, Pressure Handling.

Experience

Junior LLM Engineer Kanehl Consulting May 25 – Jul 25

•Designed and developed multi-agent AI chatbots using LangChain and LlamaIndex, enhancing user interaction capabilities

•Built and optimized Retrieval-Augmented Generation (RAG) pipelines leveraging vector databases like Chroma DB and FAISS for efficient knowledge retrieval.

•Developed backend services in Python to integrate chatbots with SQL/NoSQL databases and third-party APIs.

•Engineered data pipelines for ingesting and preprocessing manufacturing data to support real-time chatbot queries.

•Debugged and profiled chatbot systems to improve performance and ensure high-quality, context-aware responses.

•Deployed containerized applications using Docker and collaborated with cloud teams for deployment on AWS environment.

Projects Employee Turnover Prediction Project Oct 24 – Nov 24

Performed EDA, Feature Engineering, and built models (KNN, Random Forest, Decision Tree).

Prioritized recall, achieving 94.6% with the Decision Tree Model (criterion: ‘entropy’, max_depth: 10).

Used cross-validation, pipelines, and hyperparameter tuning for better generalization.

Improved skills in analytics, problem-solving and metric interpretation.

AMD vs Intel Stock Analysis

Analyzed stock performance using OLS regression with NASDAQ as a benchmark.

Calculated Sharpe ratio, revealing negative risk-adjusted returns for both stocks.

Found AMD’s Beta (1.54) and Intel’s beta (1.44), indicating strong market correlation.

Used python (Pandas, statsmodels, NumPy and Matplotlib) for analysis and visualization.

Optimizing Craft Beer Production and Sales Project Oct 24 – Nov 24

I analyzed beer quality, efficiency, and alcohol content using EDA and predictive modelling.

Built Logistic Regression and Linear Regression models.

I identified Ale as high selling but inefficient and recommended production changes.

Proposed increasing beers with 49 bitterness levels due to strong sales.

Utilized Apache Spark on Databricks for scalable, efficient analytics.

Phishing Link Detection Oct 24 – Nov 24

Developed a machine learning based web-app to identify phishing links, utilizing algorithms like Decision tree, SVM and Random Forest.

Achieved 96.4% accuracy with XGBoost, enhancing detection speed through URL analysis.

Incorporated benchmarking and third-party data sources for feature extraction and classification.

Strengthened expertise in cybersecurity, algorithm optimization, and phishing mitigation strategies.

AI-Powered Chatbot with LlamaIndex & ChromaDB Feb 25-Mar 25

Developed an AI chatbot that fetches, indexes, and retrieves knowledge from online sources.

Integrated LlamaIndex for vector-based search and Chroma DB for persistent embedding storage.

Utilized Gemini AI for intelligent question-answering and response generation.

Implemented web scraping using BeautifulSoup to extract data from Wikipedia.

Built a FastAPI-based chatbot API for real-time interactions.

Enhanced expertise in LLMs, NLP, vector databases, and AI-powered search systems.

Accomplishments

Published a Journal in FISHEREIES SCIENCE TITLED “Detection of Phishing Links using Machine Learning Techniques”.

Worked as a president of PEC CSE Club for 1 year.

Education University of Maryland, Baltimore County 4.0/4.0 Baltimore, MD Masters in Data Science Dec 2025

Panimalar Engineering College 3.58/4.0 Chennai, India

Bachelors of engineering in Computer Science May 2023

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