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Abhishek Kesari

Data Scientist • Palakol, Andhra Pradesh • a***********@gmail.com • 906****446 • linkedin.com/••••• • drivetube.ai/•••••

Professional Summary

Data Scientist with 0-1 years of experience in machine learning, deep learning, and data analysis. Hands-on experience building supervised models, CNNs, and data pipelines using Python, Pandas, Scikit-learn, TensorFlow, and visualization libraries. Seeking an entry-level Data Science role to apply model-building, feature engineering, and EDA skills to deliver actionable insights and production-ready ML artifacts.

Technical Skills

Programming Languages: Python,C,C++
Frameworks and Libraries: Pandas,NumPy,Scikit-learn,TensorFlow,Keras,OpenCV,NLTK,Matplotlib,Seaborn
Databases: SQL
Data and Analytics: Supervised Learning,Feature Engineering,Model Evaluation,Hyperparameter Tuning,Convolutional Neural Networks,Natural Language Processing,Exploratory Data Analysis,Data Cleaning,Data Preprocessing,Statistical Analysis,Dashboarding
Tools and Methodologies: Jupyter Notebook,Git,VS Code,Microsoft Azure

Work Experience

Embrizon Technologies
Remote
Data Science & AI Intern
Jun 2025 – Sep 2025
Worked at an IT services/company focusing on data analysis and insights pipelines; supported mentor-led analytics and reporting engagements.
Tech Stack: Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook, Git
  • Performed end-to-end data cleaning and preprocessing on structured datasets using Python and Pandas, implementing reusable functions to standardize inputs for analysis and modeling.
  • Conducted exploratory data analysis with Pandas and NumPy to discover feature distributions, missing-data patterns, and correlations that guided downstream feature selection.
  • Developed visualizations using Matplotlib and Seaborn to highlight trends and anomalies; delivered clear charts that improved review speed during mentor feedback sessions.
  • Prepared and transformed datasets for model training using scaling, encoding, and sampling strategies to address imbalance and improve input quality for ML experiments.
  • Authored reproducible Jupyter notebooks and documented analysis steps and assumptions, facilitating knowledge transfer and enabling mentors to replicate experiments.
  • Validated analysis artifacts and maintained version control with Git to ensure traceability of data transformations and experiment results across the team.

Projects

Customer Churn Prediction Model | Jan 2025 – Mar 2025
Tools Used: Python, Pandas, Scikit-learn, Logistic Regression, Random Forest, Hyperparameter Tuning, Matplotlib
  • Built supervised ML models (Logistic Regression and Random Forest) to predict customer churn on a telecom dataset of 7,000+ records.
  • Performed feature engineering and hyperparameter tuning, raising model F1-score from 71% baseline to 84% through targeted transformations and parameter search.
  • Documented modeling decisions, evaluation metrics, and visual summaries in Jupyter notebooks to support reproducibility and mentor review.
Sales Data Analysis Dashboard | Aug 2024 – Oct 2024
Tools Used: Python, Pandas, NumPy, Seaborn, Matplotlib, EDA
  • Cleaned and transformed 5,000+ rows of raw retail sales data using Pandas to identify seasonal trends and top-performing product categories.
  • Created a multi-chart visualization dashboard with Seaborn and Matplotlib that condensed monthly insights into a single-page summary adopted by the mentoring team.
  • Provided actionable insights on product performance and seasonality to inform potential merchandising decisions.
AI-Powered Resume Keyword Screener | Nov 2024 – Dec 2024
Tools Used: Python, NLTK, Pandas, NLP Preprocessing, Tokenization
  • Developed a Python script that parses job descriptions and resumes to compute keyword match scores and rank candidate fit, automating manual screening across 50+ job descriptions.
  • Implemented NLP preprocessing including tokenization and stopword removal using NLTK to extract meaningful skill tokens, achieving >90% term extraction accuracy in tests.
  • Packaged parsing logic into reusable functions to streamline screening across multiple hiring scenarios.
Real-Time ASL Alphabet Detection System (Final Year Project) | 2026 – 2026
Tools Used: Python, TensorFlow, Keras, OpenCV, CNN, Image Preprocessing
  • Built a real-time American Sign Language alphabet detection system using a CNN trained on a 26-class hand gesture dataset.
  • Implemented an image preprocessing pipeline (grayscale conversion, Gaussian blur, thresholding, normalization) to reduce noise and improve live detection accuracy from webcam input.
  • Added a 50-frame prediction confirmation mechanism to stabilize output and enable reliable word formation from continuous gestures.

Education

Aditya Degree College
B.Sc. Computer Science — CGPA: 8.2 • Palakol, Andhra Pradesh • 2023 – 2026
Aditya Junior College
Intermediate (MPC) — 74% • Palakol, Andhra Pradesh • 2021 – 2023
Sri Vaishnavi High School
SSC (X Class) — 89% • Andhra Pradesh • 2020 – 2021

Certifications

Python Programming — Cisco Networking Academy
C Programming — Cisco Networking Academy
Essentials of Generative AI — Microsoft
Microsoft Azure Fundamentals (AZ-900) — Microsoft
Competitive Coding (C & C++) — Criativo E-Learning Ltd.
What Is Generative AI? — LinkedIn Learning
AI Literacy for Everyone — LinkedIn Learning
Learning Microsoft 365 Copilot for Work — LinkedIn Learning
Ethics in the Age of Generative AI — LinkedIn Learning
Career Essentials in Generative AI — Microsoft & LinkedIn Learning

Achievements

  • Hackathon participation — working prototype: Participated in a hackathon conducted by Adhoc Network Company and developed a working prototype within 24 hours.
  • Algorithmic problem solving: Solved 100+ coding problems in C and C++, strengthening algorithmic thinking and implementation skills.

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