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Masthan Shaik

Data Scientist • s***************@gmail.com • +91*******253 • linkedin.com/••••• • drivetube.ai/•••••

Professional Summary

Data Scientist with 0 years of experience applying Python, machine learning, and data visualization to transform raw data into actionable insights. Skilled in data preprocessing, feature engineering, scikit-learn and TensorFlow modeling, RAG-based knowledge retrieval (FAISS, Chroma), and building interactive Power BI dashboards and AI web applications using Flask and Streamlit.

Technical Skills

Programming Languages: Python
Frameworks and Libraries: Flask,Django,Streamlit,Pandas,NumPy,scikit-learn,TensorFlow,LangChain,matplotlib
Databases: SQL,FAISS,Chroma,MySQL
Data and Analytics: Data Cleaning,ETL Concepts,Data Validation,Data Integration,Deep Learning,CNN,RNN,Power BI,Power Query,DAX
Tools and Methodologies: OpenAI,CrewAI,Groq LLMs,RAG,Git,GitHub,VS Code,Postman,Microsoft Excel
Skills: HTML5,CSS3

Work Experience

Palle Technologies
Python Full Stack & Data Science Trainee
Jun 2025 – Jan 2026
Worked at Palle Technologies, a software and training provider, developing AI-powered web applications, ML models, RAG pipelines, and business intelligence dashboards for internal and client-focused projects.
Tech Stack: Python, Flask, CrewAI, Groq LLMs, OpenAI, FAISS, Chroma, scikit-learn, TensorFlow, Pandas, NumPy, MySQL, Power BI, Power Query, DAX, Git, GitHub, VS Code, Postman, Streamlit
  • Developed AI-powered web applications using Flask, CrewAI, and Groq LLMs; implemented multi-agent workflows for automated content generation and integrated backend APIs with user-facing UI components.
  • Built machine learning models for prediction and classification using scikit-learn and TensorFlow; conducted data preprocessing, feature engineering, and model evaluation to produce production-ready models.
  • Designed and implemented Retrieval-Augmented Generation pipelines using FAISS and Chroma for vector indexing and OpenAI LLMs for context-aware responses in a personal knowledge assistant.
  • Created interactive Power BI dashboards using Power Query and DAX; connected MySQL/SQL data sources and transformed raw data into visual KPIs to support business decision-making.
  • Collaborated on application testing, debugging, and deployment using Git, GitHub, VS Code, and Postman; maintained code repositories and implemented release-ready application builds.
  • Improved data quality and pipeline reliability by implementing ETL processes, data validation, and cleaning routines in Python and SQL to ensure analysis-ready datasets for ML and BI workflows.

Projects

AI Blog Generator
Tools Used: CrewAI, Groq LLMs, Flask, Multi-agent workflows
  • Implemented a multi-agent content pipeline (Research, Writer, Editor agents) using CrewAI and Groq LLMs to generate structured, publication-ready blog posts.
  • Built a Flask-based web interface that accepts user prompts, orchestrates agent workflows, and returns refined blog drafts ready for publishing.
  • Integrated data validation and template-based formatting to ensure generated content met quality and structure requirements.
Personal AI Knowledge Assistant
Tools Used: FAISS, Chroma, OpenAI, Streamlit, RAG
  • Developed a conversational assistant capable of answering questions over personal documents by implementing RAG with FAISS/Chroma for vector retrieval.
  • Connected OpenAI LLMs to retrieved context to produce context-aware responses and implemented conversational memory in a Streamlit UI.
  • Processed PDFs and notes for indexing, including text extraction, chunking, and embedding generation for accurate retrieval.
Sales Performance Dashboard (Power BI)
Tools Used: Power BI, Power Query, DAX, MySQL, SQL
  • Designed interactive Power BI dashboards to visualize sales trends, revenue performance, and business KPIs for decision-making.
  • Performed data cleaning and transformation using Power Query and DAX to create consistent, analysis-ready data models.
  • Connected dashboards to MySQL/SQL sources and created reusable measures to support ad-hoc business reporting.
Customer Churn Prediction Analysis
Tools Used: Python, Pandas, scikit-learn, SQL
  • Processed and analyzed customer datasets using Pandas and SQL to identify churn indicators and prepare features for modeling.
  • Engineered features and trained predictive models with scikit-learn, performing evaluation and validation to identify at-risk customers.
  • Delivered visual reports and model insights to recommend retention strategies and guide stakeholder decisions.

Education

Gokula Krishna College of Engineering
Bachelor of Engineering (Computer Science) • Dec 2021 – Apr 2025

Certifications

Python Full-Stack with Data Science — Palle Technologies
MS Office with AI — Skil Nation
Prompt Engineering — Simplilearn

Achievements

  • Selected for Internship and Advanced Internship at Sun Square Technologies
  • Completed Python Full Stack & Data Science program at Palle Technologies

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