Malay S.

Key Skills

Software

Other

Top Subject Matter

Machine learning for time-series prediction and customer segmentation

Deep learning time-series forecasting

Water-quality classification with ensemble learning

Top Data Types

Document

Text

Top Task Types

Fine-tuning

Classification

Segmentation

Freelancer Overview

Machine Learning Intern, Cyfuse IIIT-delhi (Remote). Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and TensorFlow. Education includes Bachelor of Technology, Bennett University (2026) and High School (CBSE), DAV Public School (2021). AI-training focus includes data types such as Computer Code, Programming, and Document and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

Project: Water-Quality-Classification-Using-Ensemble-Learning-Techniques

OtherClassification

Developed an ensemble-based classification model to predict Water Quality Index using techniques such as Random Forest and XGBoost. Authored a research paper describing the modeling approach and achieving results strong enough for conference acceptance. The work primarily represents AI model training/evaluation for classification tasks. • Implemented ensemble learning models for water-quality classification. • Trained and evaluated models for Water Quality Index prediction. • Prepared and authored a research paper for an international conference.

2025 - 2025

Machine Learning Intern - Cyfuse IIIT-Delhi

DocumentSegmentation

Developed and optimized machine learning solutions during a remote internship focused on forecasting and customer segmentation. Worked with time-series stock prediction and applied K-means clustering to segment customers using engineered features. Collaborated with senior data scientists to productionize models, document findings, and communicate results to stakeholders for business decision-making. • Built a time-series stock predictor using feature engineering and hyperparameter tuning • Created and improved a K-means customer segmentation model using engineered features • Supported model productionization and prepared actionable documentation • Presented results to stakeholders to enable data-driven, targeted marketing decisions

2024 - 2024

Machine Learning Intern, Cyfuse IIIT-delhi (Remote)

Other

Developed and optimized machine learning solutions during a remote internship by improving model performance through feature engineering and hyperparameter tuning. Collaborated with senior data scientists to transition models toward production and to communicate findings for decision-making. The work involved iterative model development and evaluation rather than manual human annotation. • Built a time-series stock predictor and trained a K-means customer segmentation model. • Performed feature engineering and hyperparameter tuning to generalize models. • Supported productionization efforts with documentation and stakeholder-facing presentations. • Contributed actionable insights to enable targeted marketing and data-driven decisions.

2024 - 2024

Project: Stock Price Prediction Using RNN AND LSTM

Fine-tuning

Engineered a time-series forecasting model using deep learning architectures to predict stock prices for multiple NSE-listed tech companies. The activity focused on building and validating a generalized model over historical data to achieve predictive accuracy. No explicit manual data labeling is described, but the role is AI training/modeling work. • Designed and trained LSTM-based time-series forecasting for stock prediction. • Evaluated predictive accuracy and iterated for better generalization on historical data. • Worked with sequential data and model training workflows for forecasting.

2023 - 2023

Education

Work History