Highly analytical and results-driven Master of Science candidate in Applied Statistics, specializing in advanced AI/ML, Natural Language Processing (NLP), and statistical modeling. Proven ability to design and deploy scalable AI systems, optimize complex algorithms, and extract actionable insights from diverse datasets, as demonstrated by a 33% reduction in prediction errors for time-series forecasting and the development of an LLM-as-a-Judge framework. Seeking to leverage expertise in data science, machine learning, and statistical methodologies to drive innovation and solve challenging problems in a dynamic technical environment.
AI/ML Intern
Bangalore, Karnataka, India
→
Summary
Spearheaded the design and deployment of advanced AI/ML solutions, including an LLM-as-a-Judge framework and an agentic AI system, to optimize processes and evaluate model responses at Bosch Global Software Technologies.
Highlights
Designed and deployed an innovative LLM-as-a-Judge evaluation framework, enhancing the assessment of model responses on quality, relevance, and coherence for critical AI applications.
Architected a cutting-edge end-to-end agentic AI system for process optimization, meticulously aligning its development with Six Sigma DMAIC methodology.
Formally submitted an Invention Report titled “End-to-End Layered Agentic AI Framework for Process Optimization using Six Sigma (DMAIC) Approach,” demonstrating novel contributions to AI-driven process efficiency.
→
Master of Science
QMS (Applied Statistics)
Grade: 79.2%
Courses
Descriptive Statistics
Inferential Statistics
Pattern Recognition
Applied Regression
Design of Experiments
Six Sigma
Project Management
Python, Structured Query Language (SQL), Power BI, Minitab.
NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, Hugging Face, Qdrant.
Data Visualization, Statistical Modeling, Decision Tree, Bagging, Boosting, Dimensionality Reduction (PCA), Time-Series Modeling & Forecasting, Clustering, Deep Learning, Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNN), Transformers, LSTM, U-Net, YOLO, Encoder-Decoder Architectures, Generative Adversarial Network (GAN), Large Language Model (LLM), Fine-tuning, Retrieval-Augmented Generation (RAG), Agentic AI.
→
Summary
Developed an independent multimodal Retrieval-Augmented Generation (RAG) system utilizing Hugging Face, Qdrant, and Python to enhance information retrieval and context comprehension.
→
Summary
Executed a Master's project at the Indian Statistical Institute, developing a robust ensemble model to forecast cotton prices using advanced time series techniques.