Aditya Nain

Data Scientist | Machine Learning Engineer
Bengaluru, IN.

About

Highly analytical and results-driven B.Tech graduate specializing in Data Science and Machine Learning, graduated in May 2025. Proven ability to develop and deploy advanced models for financial analysis, natural language processing, and audio-based emotion recognition, significantly improving system efficiency and prediction accuracy. Eager to leverage expertise in Python, deep learning frameworks, and data engineering to drive impactful solutions in a dynamic tech environment.

Work

Xploremate
|

Data Science Intern

Remote, N/A, India

Summary

Optimized backend systems for financial data retrieval and refined query understanding, significantly enhancing user experience and data accuracy.

Highlights

Developed backend modules in Python using REST APIs to fetch stock data, reducing data retrieval time by 20%.

Achieved an 87% F1-score in intent classification by fine-tuning spaCy with rule-based preprocessing and TF-IDF features.

Built robust pipelines to collect 100K+ financial reports for 15K+ companies, ensuring 98% data accuracy and integrity.

Optimized backend integration to enable 95% real-time query success for live stock prices and financial metrics.

RGIPT
|

Summer Research Intern

N/A, N/A, India

Summary

Engineered and optimized pair trading strategies using hybrid models and smart trade execution, boosting prediction accuracy and profit.

Highlights

Engineered BiLSTM, Transformer, N-BEATS, and TCN models for pair trading, boosting prediction accuracy by 25%.

Formulated a hybrid strategy blending mean reversion and forecasts, raising profit-per-trade by 30% (F1: 0.781).

Assessed model performance rigorously using RMSE, MASE, and MAPE, with the Transformer Hybrid Strategy demonstrating superior results.

Education

Rajiv Gandhi Institute of Petroleum Technology
Bengaluru, Karnataka, India

B.Tech

Mathematics and Computing

Grade: 7.36/10

Courses

Statistical Methods and Data Analysis

Numerical Methods

Optimization Methods

Design and Analysis of Algorithms

Graph Theory

Soft Computing

Big Data Analytics

Theory of Computation

Computer Vision and Pattern Recognition

Deep Learning

Data Mining

Artificial Intelligence and Machine Learning

Skills

Programming & Tools

Python, C++, SQL, Bash, Git, Docker, AWS, GitHub Actions.

Libraries & Frameworks

Scikit-learn, TensorFlow, Keras, PyTorch, NLTK, SpaCy, Optuna, Beautiful Soup, Requests, Selenium, BiLSTM, Transformer, N-BEATS, TCN, Hugging Face (BERT), SentenceTransformers, LibROSA, SciPy, ONNX, LGBM, XGBoost, Ridge.

Data Handling & Visualization

Pandas, NumPy, Matplotlib, Seaborn, Plotly, pyLDAvis, SQLite, Excel, Statsmodels.

Machine Learning & AI

Deep Learning, Natural Language Processing (NLP), Computer Vision, Artificial Intelligence, Data Mining, Ensemble Modeling, Feature Engineering, Model Evaluation (RMSE, MASE, MAPE, F1-score, R2), Time Series Forecasting, Intent Classification, Emotion Recognition, Code Generation, Probabilistic Information Retrieval, Pair Trading Strategies.

Projects

redBus Data Decode Hackathon 2025

Summary

Achieved a top 50 ranking in the redBus Data Decode Hackathon 2025 by developing an advanced ensemble forecasting model to predict bus seat demand.

Code Generation using Probabilistic Information Retrieval

Summary

Engineered an advanced algorithm for Probabilistic Information Retrieval, enabling efficient code generation directly from natural language queries.

Emotion Recognition from Audio

Summary

Developed a fast and highly accurate emotion recognition system leveraging CNN-LSTM and audio feature fusion, optimized for edge device deployment.