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I am a PhD-trained ML Scientist (McGill) with 6+ years building, validating, and deploying production AI/ML systems. I specialize in rigorous model evaluation, multi-modal data analysis, and MLOps deployment using Docker, CI/CD, and AWS. I have built GenAI applications using LangChain, FAISS, and OpenAI APIs, emphasizing thorough testing and validation frameworks. I have published 8+ papers demonstrating deep technical rigor and collaborated with clinicians and imaging scientists to translate research into clinical trial optimizations. I am seeking senior roles in GenAI validation, model risk management, or AI governance.…I am a PhD-trained ML Scientist (McGill) with 6+ years building, validating, and deploying production AI/ML systems. I specialize in rigorous model evaluation, multi-modal data analysis, and MLOps deployment using Docker, CI/CD, and AWS. I have built GenAI applications using LangChain, FAISS, and OpenAI APIs, emphasizing thorough testing and validation frameworks. I have published 8+ papers demonstrating deep technical rigor and collaborated with clinicians and imaging scientists to translate research into clinical trial optimizations. I am seeking senior roles in GenAI validation, model risk management, or AI governance.

Neda Shafiee

Data Scientist, AI Engineer, AI Developer, +2

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I am a PhD-trained ML Scientist (McGill) with 6+ years building, validating, and deploying production AI/ML systems. I specialize in rigorous model evaluation, multi-modal data analysis, and MLOps deployment using Docker, CI/CD, and AWS. I have built GenAI applications using LangChain, FAISS, and OpenAI APIs, emphasizing thorough testing and validation frameworks. I have published 8+ papers demonstrating deep technical rigor and collaborated with clinicians and imaging scientists to translate research into clinical trial optimizations. I am seeking senior roles in GenAI validation, model risk management, or AI governance.…I am a PhD-trained ML Scientist (McGill) with 6+ years building, validating, and deploying production AI/ML systems. I specialize in rigorous model evaluation, multi-modal data analysis, and MLOps deployment using Docker, CI/CD, and AWS. I have built GenAI applications using LangChain, FAISS, and OpenAI APIs, emphasizing thorough testing and validation frameworks. I have published 8+ papers demonstrating deep technical rigor and collaborated with clinicians and imaging scientists to translate research into clinical trial optimizations. I am seeking senior roles in GenAI validation, model risk management, or AI governance.

Available to hire

I am a PhD-trained ML Scientist (McGill) with 6+ years building, validating, and deploying production AI/ML systems. I specialize in rigorous model evaluation, multi-modal data analysis, and MLOps deployment using Docker, CI/CD, and AWS.

I have built GenAI applications using LangChain, FAISS, and OpenAI APIs, emphasizing thorough testing and validation frameworks. I have published 8+ papers demonstrating deep technical rigor and collaborated with clinicians and imaging scientists to translate research into clinical trial optimizations. I am seeking senior roles in GenAI validation, model risk management, or AI governance.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Doctoral Researcher at McGill University, Montreal Neurological Institute

January 1, 2019 - January 1, 2025

Developed ML models on 300+ participants combining MRI features and cognitive tests for Alzheimer's disease risk prediction, achieving 0.84 AUC and reducing false positive rate by 22% compared to cognitive-only approaches. Built Python infrastructure integrating ANTs, FreeSurfer, and custom statistical models with bash orchestration for automated longitudinal analysis of 4000+ scans. Deployed a RAG-based research assistant using LangChain, FAISS, and OpenAI APIs with Docker containerization and CI/CD via GitHub Actions, accelerating literature review workflows. Collaborated with neurologists, statisticians, and imaging physicists to translate research into clinical trial optimizations, achieving 3.8x sample size reduction and 56% fewer patient visits.

Image Processing Intern at NeuroRx Research

January 1, 2022 - Present

Built and deployed an automated ML model to classify daily incoming MRI images as new or follow-up patients using image cross-correlation and lesion-based features, streamlining data management for the neuroimaging research pipeline.