Thanusri Aenugula

Data Scientist & AI Research Engineer

Transforming complex data into actionable insights through advanced machine learning and predictive analytics.

3+ Years Experience

94% Prediction Accuracy

80% Testing Time Reduction

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+91 9398892690

Driving Data-Driven Solutions

Currently pursuing Master's in Data Science at Texas A&M University, I specialize in building predictive models and machine learning systems that solve real-world problems. My experience spans from automotive testing optimization at Daimler to renewable energy analytics, consistently delivering high-accuracy models with measurable business impact.

Education

MS Data Science

Texas A&M University (2025-2026)

BE Electrical Engineering

SSN College (8.72 CGPA)

Data Science Certification

UT Austin McCombs (3.75 GPA)

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Technical Expertise

Languages

Python

C++

SQL

Tools

PowerBI

Tableau

Azure DataBricks

Azure ML

Libraries

TensorFlow

PyTorch

PySpark

Pandas

Core Strengths

Leadership Problem Solving Analytical Thinking

Where I've Made Impact

Daimler India Commercial Vehicles

R&D Intern (Jan-Jun 2025)

Built ML models for dynamic brake testing achieving 94% R² accuracy for deceleration prediction and 93% for stopping distance. Reduced testing time by 80% through statistical twin development. Developed real-time monitoring pipelines using Azure Databricks for telematics analysis.

94% Prediction Accuracy

80% Time Reduction

Shrimitha Energy Solutions

Data Science Intern (Sept-Nov 2024)

Created hybrid CNN-LSTM-XGB models for battery life prediction with 97% R² accuracy. Analyzed 38+ batteries reducing failure prediction time by 25%.

97% Battery Model Accuracy

25% Faster Predictions

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Projects That Deliver Results

Predictive Maintenance of Wind Turbine Systems

Research project submitted to Elsevier Engineering Applications of AI journal. Built hybrid CNN-LSTM-GRU model achieving 91% coefficient of determination for turbine life prediction. Created interactive PowerBI dashboard for real-time monitoring.

91% Accuracy

CNN-LSTM Hybrid Model

Technology Stack

Python TensorFlow CNN LSTM PowerBI

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Behavioral Churn Modeling

Achieved 98% churn prediction accuracy using Gradient Boosting, enabling 15% churn reduction through targeted retention strategies. Validated with cross-validation showing <10% variance.

98% Accuracy

15% Churn Reduction

Technology Stack

Python Scikit-learn Gradient Boosting Pandas NumPy

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Sales Analysis & Forecasting

Improved wine sales forecasting accuracy by 20% through time series analysis. Compared 7 forecasting techniques with Triple Exponential Smoothing achieving lowest RMSE of 317.43.

20% Improvement

317.43 Lowest RMSE

Technology Stack

Python Time Series Exponential Smoothing Matplotlib Seaborn

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Ready to Collaborate?

I'm actively seeking data science opportunities and research collaborations. Whether you're looking for predictive analytics expertise, machine learning solutions, or innovative AI research, let's discuss how we can create impactful results together.

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Currently based between Texas and India, available for remote collaboration and on-site opportunities.

Available for Summer 2026 opportunities