Topic on request
Developed a table-aware RAG framework to make structured industrial data searchable and usable by LLMs, automating extraction and reducing manual data prep by ~60%.
Fine-tuned pre-trained embedding models and optimized retrieval pipelines, increasing retrieval precision by ~25% and balancing scalability with cost efficiency.
Built and monitored evaluation pipelines to ensure model reproducibility and measurable progress, collaborating with cross-functional teams to deliver research for production systems.
10/2025
Data Science
Master of Science
Friedrich-Alexander-University of Erlangen-Nuremberg
10/2020
Information Technology
Bachelor of Technology
Sri Venkateswara College of Engineering affiliated by JNTUA Ort: Tirupati, India
Certifications:
Microsoft Certified: Power BI Data Analyst Associate
Data Analysis with R Programming
Multi AI Agent Systems with crewAI
Google Data Analytics
Tecnical Skills
AI/ML:
PyTorch
scikit-learn
Transformers
NLP, LLMs
RAG, LangChain
Hugging Face
Reporting
Power BI
Tableau
Excel (advanced, VBA)
PowerPoint
Data Engineering:
ETL pipelines
data modeling
PySpark
dbt
Airflow
Snowflake
Databricks
Cloud/DevOps:
FastAPI
REST
Docker
Kubernetes
CI/CD
AWS
Azure
Git
Core Strengths:
Analytical
Thinking
Problem Solving
Collaboration
Learning Agility
Personal Projects:
Customer Churn Prediction Model:
Built a classification model in Python using Pandas, scikit-learn, and SHAP to predict customer churn and uncover the key factors driving it.
Created an interpretable Power BI dashboard that visualized churn probabilities by customer segment, helping define targeted retention strategies.
Predictive Maintenance Automation:
Developed a time-series machine learning pipeline in Python using XGBoost and scikit-learn to predict equipment failures before they occurred.
Applied data validation and feature selection to boost model accuracy, and integrated results into Power BI dashboards to support proactive maintenance planning.
Traffic Accident Risk Analytics:
Designed and implemented an ETL pipeline and SQL-based data model to analyze accident patterns across Bavaria.
Built Power BI visualizations highlighting high-risk zones, seasonal trends, and contributing factors, enabling data-informed traffic safety decisions.
Topic on request
Developed a table-aware RAG framework to make structured industrial data searchable and usable by LLMs, automating extraction and reducing manual data prep by ~60%.
Fine-tuned pre-trained embedding models and optimized retrieval pipelines, increasing retrieval precision by ~25% and balancing scalability with cost efficiency.
Built and monitored evaluation pipelines to ensure model reproducibility and measurable progress, collaborating with cross-functional teams to deliver research for production systems.
10/2025
Data Science
Master of Science
Friedrich-Alexander-University of Erlangen-Nuremberg
10/2020
Information Technology
Bachelor of Technology
Sri Venkateswara College of Engineering affiliated by JNTUA Ort: Tirupati, India
Certifications:
Microsoft Certified: Power BI Data Analyst Associate
Data Analysis with R Programming
Multi AI Agent Systems with crewAI
Google Data Analytics
Tecnical Skills
AI/ML:
PyTorch
scikit-learn
Transformers
NLP, LLMs
RAG, LangChain
Hugging Face
Reporting
Power BI
Tableau
Excel (advanced, VBA)
PowerPoint
Data Engineering:
ETL pipelines
data modeling
PySpark
dbt
Airflow
Snowflake
Databricks
Cloud/DevOps:
FastAPI
REST
Docker
Kubernetes
CI/CD
AWS
Azure
Git
Core Strengths:
Analytical
Thinking
Problem Solving
Collaboration
Learning Agility
Personal Projects:
Customer Churn Prediction Model:
Built a classification model in Python using Pandas, scikit-learn, and SHAP to predict customer churn and uncover the key factors driving it.
Created an interpretable Power BI dashboard that visualized churn probabilities by customer segment, helping define targeted retention strategies.
Predictive Maintenance Automation:
Developed a time-series machine learning pipeline in Python using XGBoost and scikit-learn to predict equipment failures before they occurred.
Applied data validation and feature selection to boost model accuracy, and integrated results into Power BI dashboards to support proactive maintenance planning.
Traffic Accident Risk Analytics:
Designed and implemented an ETL pipeline and SQL-based data model to analyze accident patterns across Bavaria.
Built Power BI visualizations highlighting high-risk zones, seasonal trends, and contributing factors, enabling data-informed traffic safety decisions.