Hello, everyone! My name is Nihar Ranjan Panda and I am from the eastern part of India. I was born and raised in a town beside Asia's largest brackish lagoon. However, I have been living in and around Delhi for the past five years. I hold a Bachelor of Technology degree in Mechanical Engineering from the National Institute of Technology, Rourkela.

After my graduation, I worked at IndianOil in the commercial division of India's largest government-owned refinery. I was looking after the capital procurement and material demand forecasting for various technical departments within the refinery. Moreover, I had the privilege of leading a team of four members looking after the whole procurement process which consists of managing tenders, conducting negotiations, and overseeing procurements worth $16 million.

Highly motivated and results-oriented professional with 4+ years of experience in capital procurement, trading strategies, and data science. Proven ability to lead teams, negotiate contracts, automate processes, and leverage data to drive strategic decision-making.

I have a passion for cooking, playing chess, and lifting weights. I also consider myself a geography whiz - I can recite all the countries and their capitals under 10 minutes 🤩.

• Python: NumPy, Pandas, Matplotlib, Seaborn, Regex, Scikit-Learn, Statsmodels, Streamlit, NLTK, Hugging Face, LangChain
• R
• SQL: BigQuery, PostgreSQL, MySQL
• ETL Pipelines: Extract, Transform, Load
• Version Control: Git
• Visualization & Dashboarding: Tableau, Power BI, Streamlit
• Databases: Neo4j, MongoDB
• Cloud Platforms: GCP, AWS

• Supervised Learning: Regression, Classification, Decision Trees, SVM, Ensemble Methods (Random Forest, AdaBoost, XGBoost)
• Unsupervised Learning: K-Means, Hierarchical Clustering, PCA, Truncated SVD, DBSCAN, t-SNE
• Natural Language Processing (NLP): Text Preprocessing, Named Entity Recognition, Sentiment Analysis, Topic Modeling
• Generative AI & Large Language Models (LLMs): Fine-tuning, RAG, Prompt Engineering, Chain-of-Thought Reasoning
• Deep Learning: ANN, CNN, RNN, LSTM, Transformer Models
• MLOps & Model Deployment: MLFlow, Airflow, Vertex AI, Docker, Kubernetes

• Statistical Analysis & Data-Driven Decision Making
• Descriptive & Predictive Analytics
• Time Series Forecasting
• End-to-End AI Model Development & Deployment

• Problem-Solving & Critical Thinking
• Effective Communication & Stakeholder Management
• Collaborative Team Player
• Data-Driven Analytical Approach

Feel free to add this to your README file, and let me know if you'd like any additional tweaks! 🚀