Tamilarasan .

Data Associate L1 @ Infotact Solutions | Building AI-Powered Solutions · Chennai, Tamil Nadu, India

I am a highly logical and pragmatic data professional driven by a deep curiosity to master complex analytical challenges. With a collaborative spirit, I am ambitious about leveraging advanced data techniques to build robust, high-performing predictive models and deliver impactful, data-driven solutions.

Experience

Data Associate L1

Infotact Solutions · May 2026 – Present

Not yet confirmed

• Project 1: Manufacturing & Automotive – Contextual Predictive Maintenance (IoT Edge AI)
• Built an AI-powered predictive maintenance system by integrating IoT sensor data with external contextual information to predict equipment failures.
• Performed time-series preprocessing, feature engineering, and trained LightGBM/XGBoost models using SMOTE and cross-validation.
• Applied SHAP explainability and optimized model performance for proactive maintenance decisions.
• Project 2: Travel & Hospitality – Reinforcement Learning for Dynamic Pricing
• Developed a reinforcement learning-based dynamic pricing system using a custom OpenAI Gym environment.
• Implemented Q-Learning and Deep Q-Network (DQN) agents to optimize pricing strategies and maximize revenue.
• Evaluated model performance against baseline pricing methods and visualized pricing policies through interactive analytics.
• Project 3: Construction & Real Estate – Geospatial Valuation via Spatial Embeddings
• Built a geospatial property valuation model using spatial embeddings and graph-based machine learning techniques.
• Processed geospatial datasets with GeoPandas, engineered location-based features, and compared XGBoost with Graph Neural Network (GNN) models.
• Developed interactive dashboards to visualize property price predictions and neighborhood insights.
• • Maintained structured GitHub version control with regular commits, issue tracking, and documentation following industry-standard ML engineering practices.
• • Collaborated in an Agile environment while documenting experiments, model performance, and technical workflows.

X

Data Science & AI Intern

XYlofy AI · May 2026 – Jul 2026

Not yet confirmed

• • Collected, cleaned, and analyzed structured and unstructured datasets using Python, Pandas, NumPy, and SQL.
• • Performed Exploratory Data Analysis (EDA) to identify trends, patterns, and actionable business insights.
• • Developed Machine Learning models for prediction and classification using Scikit-learn, XGBoost, and Random Forest.
• • Applied feature engineering, data preprocessing, and model evaluation techniques to improve predictive performance.
• • Built AI-powered analytics solutions and automated data workflows for efficient decision-making.
• • Created interactive dashboards and visualizations using Power BI, Matplotlib, and Seaborn.
• • Collaborated with mentors and cross-functional teams using Git and Agile methodologies to deliver end-to-end AI solutions.
• • Documented project workflows, model performance, and technical findings for stakeholders.

D

Data Analyst

Decodelabs · May 2026 – Jun 2026

Not yet confirmed

• • Collected, cleaned, and transformed structured and unstructured datasets using Python, Pandas, NumPy, and SQL.
• • Performed Exploratory Data Analysis (EDA) to identify trends, patterns, and business insights.
• • Built interactive dashboards and reports using Power BI to visualize key performance indicators.
• • Applied statistical analysis and data visualization techniques to support data-driven decision-making.
• • Automated data preprocessing and reporting workflows, reducing manual effort and improving efficiency.
• • Collaborated with mentors and team members to deliver analytics projects within project timelines.
• • Documented analytical processes, findings, and recommendations for stakeholders.

X

AI & Data Analyst Intern

XYlofy AI · Apr 2026 – May 2026

Not yet confirmed

• Worked as an AI & Data Analyst Intern at Xylofy AI, contributing to real-world machine learning and data analytics projects.
• Developed predictive models, performed data preprocessing and analysis, and built AI-driven solutions using Python and machine learning techniques.
• • Built machine learning projects including Customer Churn Prediction, Sentiment Analysis, and Fraud Detection Systems
• • Performed data cleaning, feature engineering, exploratory data analysis, and model evaluation
• • Worked with ML algorithms, NLP techniques, and Explainable AI methods for business problem solving
• • Developed interactive dashboards and analytical visualizations for decision making
• • Collaborated in a remote project-based internship environment and gained practical industry experience

I

AI Intern

Infosys Springboard · Nov 2025 – Jan 2026

Not yet confirmed

• Internship 6.0 (B6-7): Agri Yield Predictor
• Development of an Application to Forecast Crop Yield Using Environmental and Soil Data
• Developed a predictive application designed to estimate agricultural crop yield using environmental and soil-related parameters.
• The system analyzes factors such as soil quality, temperature, rainfall, humidity, and nutrient levels to generate accurate yield predictions.
• The project involved data collection, preprocessing, exploratory data analysis, and machine learning model development to identify patterns affecting crop productivity.
• The application helps farmers and agricultural planners make data-driven decisions for crop selection and yield optimization.
• Key Features:
• Crop yield prediction using machine learning models
• Integration of environmental and soil datasets
• Data preprocessing and exploratory data analysis (EDA)
• Visualization of agricultural insights for better decision making
• Technologies Used:
• Python | Machine Learning | Data Analysis | Pandas | NumPy | Matplotlib | Scikit-learn