Hi 👋 My name is Rakshak Kunchum

💡 ML | NLP | DL | GenAI

🎓 Master of Science Data Science at Northeastern University

🚀 Data Science Intern at Capital One (Summer 2024)

Experience

• Data Science Intern @ Capital One, McLean, Virginia, USA (June 2024 - August 2024)
• Data Science Engineer @ DataWeave, Bangalore, India (July 2020 - October 2022)
• Data Engineering Intern @ DataWeave, Bangalore, India (January 2020 - June 2020)

Intro

• 🌍  I’m based in Boston, MA
• 🖥️  See my portfolio at Rakshak Kunchum
• ✉️  You can contact me at krakshak7698@gmail.com
• 🔭 I’m fascinated by the application of Machine Learning and Deep Learning to solve real-world problems!
• 🧠  I’m learning Deep Learning, Algorithms and LLMs
• 🤝  I’m open to collaborating on Data Science
• ⚡  I use tabs over spaces
• ⚽ My hobbies include hiking, volleyball, badminton, video games, e-sports

Projects

• FinAdvisor - Personal Finance QA advisor, Fine-tuned LLaMA 7B and Mistral 7B LLM models, Gradio UI, Chroma Vector DB
• Forecasting Credit Card Expenditure - Random Forest and XGBoost Regressors (R²: 0.86, explained_variance_score: 0.83)
• Multimodal Sentiment Analysis with Vision-Language Models - Few-shot learning, prompt engineering, Streamlit UI
• Distracted Driver Detection - CNN, Object Detection, Object Classification (Validation Accuracy: 0.825)

Achievements & Honors

• 1st place at Harvard Data Science Initiative’s Agri Data Hackathon 2024 in sponsorship with USDA NASS (Hackathon results)
• 3rd place at Northeastern University Data Science Hackathon Summer 2023.

Skills

Programming Languages

Python R SQL Java

Databases

MySQL MongoDB SnowFlake

Python Frameworks for Data Science

Numpy Pandas Tensorflow Sklearn PyTorch Dask OpenCV Matplotlib Seaborn

Toolkits/Frameworks

Apache Airflow Apache Kafka Docker Amazon Web Services Git Tableau

Data Engineering

Selenium Shell Scripting Web Scraping Data Analysis Data Wrangling

About Me

In my role as a Data Scientist Intern at Capital One, I led a fraud detection project that automated feature creation, algorithm optimization, and visualization for Supplemental Fraud SAR risk sloping. This work saved 20 FTE investigators’ effort and nearly $1M in operational costs while achieving a ROC-AUC score of 0.8. My contributions went beyond technical implementation by effectively communicating results to cross-functional teams, and turning complex data insights into actionable strategies. Formerly a Data Science Engineer at Dataweave, an e-commerce analytics company for 2.5 years. In this role, I was initially involved in Data scraping and Data Wrangling from major e-commerce websites. As I progressed, I transitioned into automating data workflows using technologies like Apache Airflow and Amazon Web Services. This hands-on experience allowed me to witness the direct application of Data Science in solving real-world problems.

I completed my undergraduate in Information Science Engineering at BMS College of Engineering, graduating in the top 10% of the class of 2020. My research interests include Neural Networks and Computer Vision. At BMSCE, I was a senior coordinator for the ISE Student Club and served as the Technical mentor between 2019-2020. From a cultural standpoint, I served as the Coordinator and Head of the publicity and sponsorship teams for the BMSCE’s annual technial symposium, PhaseShift between 2018-2019.

Outside of my academic and professional pursuits, I enjoy staying active and exploring the outdoors. Whether it’s playing badminton or embarking on a scenic trail hike, I find solace in the balance between mental and physical well-being. Also, I find immense joy in immersing myself within captivating story-driven games, where I often happily venture into virtual worlds, indulging in epic gaming adventures.

Looking ahead, I am eager to combine the comprehensive skills in Data Science, encompassing Machine Learning, Statistics, and Algorithms, that I am acquiring at the university with my prior industry experience. My ultimate goal is to tackle complex real-world data science problems and make a meaningful impact.

University Courses

Spring 2023

• DS5110: Introduction to Data Management and Processing.
• DS5220: Supervised Machine Learning.

Summer 2023

• DS5230: Unsupervised Machine Learning and Data Mining.

Fall 2023

• CS5800: Algorithms.
• CS7150: Deep Learning.

Spring 2024:

• CS6120: Natural Language Processing.
• CS6200: Information Retrieval.

Fall 2024:

• DS5500: Data Science Capstone.

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