Machine Learning Data Governance
Resume:
Amir Rahnamai Barghi
Thornhill, Ontario, Canada Tel. 647-***-**** email: *********@*****.*** https://www.linkedin.com/in/amir- rahnama-ph-d-939188229/
Status: Canadian Citizen
SUMMARY
Skilled Machine Learning Developer with a Ph.D. in Mathematics and a Master's in Computer Science, bringing over six years of experience in Python-based machine learning. Expert in developing and implementing financial risk models, conducting AI research, and applying machine learning to business solutions. Proficient in fine-tuning Large Language Models such as Bert, LLaMA 2, PaLM2, and FinBERT for diverse applications. Demonstrated ability to deliver high-accuracy models for risk assessment, utilizing deep neural networks and advanced algorithms. Committed to driving innovation in financial and credit risk management through machine learning and leveraging OpenAI's APIs for cutting-edge solutions. TECHNICAL SKILLS
• Data Visualization: Matplotlib, Seaborn, pyLDAVis
• Python IDEs including: Jupyter Notebook, PyCharm, VS Code
• Machine Learning: Logistic Regression, Decision Trees, Naïve Bayes, SVM, Random Forest, Deep Neural Network (RNN, CNN), NLP (NLTK), Feature Engineering
• Frameworks: Flask, TensorFlow, PyTorch, Keras
• Web application utilizing Streamlit
• Agile Methodologies: Agile Scrum Mastery, experience in project management with cross-functional teams.
• Data Governance: Proficient in data quality, metadata management, and data privacy in compliance with Responsible AI standards.
PROFESSIONAL WORK EXPERIENCE
Part-Time Instructor, Queen's College of Business and Technology Jan. 2024 to present
• Deliver lectures and practical sessions in business and technology courses, focusing on the application of machine learning and AI in contemporary business practices. Consultant for Private Clients Jul. 2023 to present
• Develop Assistant AI solutions for business applications, utilizing Large Language Models and machine learning to analyze and track industry trends. Due to non-disclosure agreements, specific project details are confidential. Manager Data Scientist, AI Risk-Financial Services Risk Management Jul. 2022– May 2023 Ernst & Young LLP (EY)
• Implemented TensorFlow and PyTorch for BERT model fine-tuning, enhancing credit risk assessments.
• Utilized Agile Scrum methodologies to manage projects across teams, facilitating alignment with business objectives and regulatory standards.
• Co-authored AI standards and MLOps documentation for financial risk management.
• Applied TensorFlow and PyTorch to fine-tune BERT models for financial sentiment analysis
• Led data scientist team to work on projects including stress testing and credit risk modeling for Mortgage and HELOC portfolios.
• Published internal documents regarding MLOps for financial risk management. Data Scientist, NLP specialist Jun. 2021 - Nov. 2021 The government of Canada, Justice Canada, Business Analytics Centre
• Fine-tuned BERT models, optimized model efficiency for immigration document similarity.
• Presented expertise in "NLP for Legal Documents" to stakeholders.
• Fine-tuned BERT language models, enhancing accuracy through model parameter adjustments for immigration-related text.
• Employed Agile and Scrum frameworks to drive project completion, ensuring clear communication and alignment of cross-functional teams on data governance and security protocols.
• Employed advanced data preprocessing techniques including text cleaning, tokenization, and lemmatization, optimizing model efficiency for immigration document similarity models. Consultant Data Scientist, NLP specialist Dec. 2020 - Mar. 2021 The government of Canada, Justice Canada, Business Analytics Centre
• Initiated and defined diverse business projects in the legal domain.
• Leveraged TensorFlow to achieve state-of-the-art results in text summarization for legal documents, showcasing proficiency in deep learning architectures.
• Utilized PyTorch to fine-tune language models for tasks like Immigration Document Similarity, showcasing versatility in implementing effective NLP solutions.
• Data Scientist, NLP Specialist Dec. 2019 - Sept. 2020 The government of Canada, Transport Canada, Economic Group (ACA)
• Automated and optimized the trade process by replacing manual Harmonized System (HS) classification with text classification using machine learning algorithms.
• Conducted data cleaning on textual data using NLTK and NLP libraries.
• Analyzed and visualized data using Matplotlib and Seaborn.
• Built Random Forest, SVM, CNN, RNN models on cleaned marine free text to predict 6 digits HS code of commodities and goods imported to Canada via Marine.
Data Scientist Apr. 2019 - Sept. 2019
The government of Canada, Statistics Canada
• Conducted research in ML and NLP fields to achieve data analytics solutions.
• Utilized large amounts of unstructured text and structured business web-scraping datasets from different retailers such as Marks, Old Navy, Sport Check, and The Bay.
• Identified the correct algorithms and methodology for a variety of ongoing projects at StatsCan.
• Machine Learning: Supervised algorithms - Support Vector Machines (SVM), Random Forest, Recurrent Neural Networks (RNN)
Data scientist Sept. 2018 - Apr. 2019
York University & ACS company, Toronto
• Contributed significantly to the development of a real-time monitoring system for driver vigilance, focusing on managing fatigue and enhancing road safety.
• Investigated eye-tracking data characteristics, integrating simultaneously recorded EEG data to develop a non-intrusive measure of driver behavior, with a specific focus on identifying indicators of drowsiness.
• Implemented and validated supervised machine learning models (ANN, CNN, XGBOOST, SVM, Random Forest) for predicting driver drowsiness, ensuring reliability for business applications. Entry Level Machine Learning Developer Sept. 2016 - Aug. 2018 Software Solution Inc., Richmond Hill, ON
• Collected and meticulously cleaned both structured and unstructured data from diverse resources, ensuring high data quality for subsequent analysis.
• Machine Learning Model Development: Spearheaded the development of statistical and machine learning models based on a comprehensive training dataset, achieving exceptional results with a perfect accuracy score. ACADEMIA RESEARCH AND TEACHING EXPERIENCES
• Instructor at Queen's College, York University, Carleton University, and K.N. Toosi University, teaching Python, Calculus, Linear Algebra, Differential Equations, Graph Theory, Number Theory, Machine Learning, Algorithms, Data Analysis, and C++ programming.
• Associate Professor at K.N. Toosi University of Technology, with a decade-long tenure, covering a wide array of mathematics and computer science courses.
• Published over 35 mathematics papers and collaborated internationally, with research focused on mathematical sciences and machine learning, including a notable publication on musical preferences prediction.
• Adjunct Research Professor at Carleton University, supervised 48 graduate students, served as a reviewer for the American Mathematical Society, and presented at international conferences in multiple countries.
• Most of My publication available at Scholar: https://scholar.google.ca/citations?user=555TTQUAAAAJ&hl=en EDUCATION
• Master of Computer Science, University of Ottawa, ON, Canada
• Ph.D. in Mathematics, TMU University, Tehran, Iran ACADEMIC AND PROFESSIONAL HONORS
• Excellent Graduate Scholarship, Ottawa University, Ottawa ON
• Ontario Graduate Scholarship, Ottawa University, Ottawa ON
• Research Association, Carleton University, Ottawa ON
• Research Fellowship: ICTP, Trieste Italy
SELF-STUDY COURSES
• Developments in Financial Crime Compliance: Study Online Manchester
• Willing to enrolment in Anti-Money Laundering Specialists (ACAMS) Designation course
• Financial Derivatives: A Quantitative Finance View, Udemy Online Course
• Google Cloud Fundamentals Core Infrastructure: Google Cloud Skills
• Introduction to AI and Machine Learning on Google Cloud: Google Cloud Skills
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