About
Highly driven Data Scientist with a strong bioinformatics foundation, poised to deliver cutting-edge ML and Generative AI solutions. Bringing hands-on experience in analyzing large-scale transcriptomic datasets (600+ bulk samples, >150k single cell profiles) and developing end-to-end data pipelines. Eager to apply expertise in 20+ ML, deep learning, and GenAI projects, including LangChain and RAG systems, to translate complex research into scalable, reproducible, and production-aligned solutions using Python, SQL, Docker, and MLflow.
Work
ERES Biotechnology
|Bioinformatics Instructor
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Summary
Designed and delivered practical bioinformatics workshops, equipping life science professionals with essential Python-based data analysis and reproducible research skills.
Highlights
Designed and delivered specialized workshops on Python for data analysis tailored to life science professionals, covering NumPy, Pandas, data visualization, and reproducible workflows.
Prepared comprehensive hands-on exercises and course materials, emphasizing reproducible research practices, scripting best practices, version control, and advanced data-cleaning techniques.
Mentored numerous participants in applying advanced bioinformatics techniques to their unique datasets, providing individualized feedback to significantly strengthen their reproducible research workflows.
Skilled Hub
|Data Science & AI Intern
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Summary
Leading the development of production-aligned ML and GenAI solutions, leveraging advanced techniques to automate processes and enhance data workflows for real-world applications within a dynamic startup environment.
Highlights
Developed and deployed 20+ real-world ML, deep learning, and generative AI projects (NLP, computer vision), delivering production-aligned models for classification, forecasting, and summarization.
Engineered and implemented LangChain-based AI agents and Retrieval-Augmented Generation (RAG) pipelines, significantly automating document summarization and customer-support functions.
Fine-tuned transformer models using LoRA and Hugging Face Transformers, establishing robust training and evaluation pipelines with PyTorch and MLflow for enhanced model performance.
Constructed comprehensive end-to-end data workflows for ingestion, cleaning, feature engineering, and validation using Python, Pandas, NumPy, and SQL, automating model evaluation and monitoring.
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Summary
Conducted advanced genomic data analysis and developed reproducible bioinformatics pipelines, leading to the identification of disease biomarkers and improved prognostic assessments for cancer research.
Highlights
Analyzed extensive transcriptomic datasets, including 600+ bulk samples and over 150k single-cell profiles, successfully identifying candidate cancer biomarkers and molecular signatures.
Developed and deployed reproducible bioinformatics pipelines utilizing Nextflow and Docker for High-Performance Computing (HPC) environments, enabling scalable and portable analyses across diverse computational clusters.
Implemented advanced Cox proportional-hazards survival models, deriving critical risk scores to accurately stratify patient cohorts and support robust prognostic assessments for improved clinical insights.
Skills
Programming
Python (Pandas, NumPy, Scikit-learn, PyTorch), R, Bash, Linux.
Machine Learning
Linear/Non-Linear Regression, SVM, KNN, Decision Trees, K-Means Clustering.
Deep Learning & Generative AI
CNN, RNN, Transformer Architectures, BERT/GPT Fine-tuning.
AI Engineering
RAG (Retrieval-Augmented Generation), LangChain, AI Agents.