Davoud Gholamian

Associate Director of Innovation Execution & Lead AI Product Owner
Royal Bank of Canada · Toronto, ON, Canada

📧 dgholamian@gmail.com · 🔗 LinkedIn · 🐙 GitHub · 🌐 Website · 🎓 Google Scholar

About

AI Product and Innovation Leader with deep expertise in Generative AI, Large Language Models (LLMs), Machine Learning, and enterprise AI transformation. Proven track record of leading cross-functional teams and delivering AI products that drive operational excellence, enhance decision-making, and accelerate business outcomes across banking, risk management, and enterprise functions.

Combines deep technical expertise, product leadership, and research excellence to transform emerging AI capabilities into scalable business solutions. Experienced in AI strategy, product ownership, executive stakeholder engagement, and workforce enablement, with a strong record of driving AI adoption from concept to enterprise deployment. Active researcher and educator with publications in federated learning and deep learning, and faculty experience teaching graduate-level AI, machine learning, and deep learning courses.

Key Achievements

Metric	Value
Annual Business Value Delivered (RBC)	$1.8M+
Enterprise Platform Users Enabled	700+
Employees Trained in AI	400+
Peer-Reviewed Publications	20+
Academic Citations	480+
Cost Saved at Royan Institute	$1M

Professional Experience

Associate Director, Innovation Execution & Lead AI Product Owner

Royal Bank of Canada (RBC) · Toronto, ON, Canada · 2025 – Present

Delivered AI products and automation solutions saving more than $1.8M in annual business value across Enterprise Risk, Market Risk, Counterparty Credit Risk, Operational Risk, and Learning & Talent functions.

Lead cross-functional teams of AI engineers, data scientists, developers, and business stakeholders in the design, development, and delivery of enterprise AI products.
Delivered a risk model attestation platform used by 700+ employees, automating regulatory and governance workflows in Enterprise Risk — generating approximately $300K in annual operational savings.
Led the development of a risk gap analysis solution that streamlined assessment and compliance activities across multiple risk functions — delivering approximately $500K in annual cost savings.
Led the development of an AI-powered risk intelligence chatbot enabling interactive identification and analysis of model risk issues in Operational Risk — generating approximately $150K in annual business value.
Led the delivery of a generative AI assistant for Market Risk teams with natural language querying, data visualization, and real-time integration with enterprise APIs and databases — creating approximately $720K in annual operational value.
Led the delivery of an AI benchmarking platform capable of extracting and analyzing information from large-scale regulatory and risk documentation — generating approximately $50K in annual efficiency gains.
Automated a critical Enterprise Risk compliance pipeline, improving governance efficiency and delivering approximately $100K in annual savings.
Designed and delivered two GRM-wide AI training programs that educated 400+ employees on AI fundamentals and AI opportunity identification aligned with RBC’s strategic objectives.
Developed a workforce analytics and data mining dashboard leveraging 500,000+ learning records, uncovering actionable organizational insights and generating approximately $35K in annual efficiency gains.
Facilitated executive-level AI workshops and innovation sessions for senior leaders, including VPs, SVPs, and EVPs, accelerating AI adoption and enterprise-wide AI transformation initiatives.

Part-time Faculty Member — Deep Learning & AI for Software Developers

Seneca Polytechnic · Toronto, ON, Canada · 2026 – Present

Deliver graduate-level instruction in Deep Learning, covering feedforward neural networks (FFNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTM and GRU architectures, generative adversarial networks (GANs), Transformers, transfer learning, and modern AI applications.
Design and develop the AI for Software Developers curriculum, integrating machine learning fundamentals, data preprocessing and analysis with Pandas, exploratory data analysis (EDA), feature engineering and scaling, supervised and unsupervised learning, model evaluation and validation, machine learning lifecycle management, and practical handling of categorical and numerical data.

Research Assistant — Machine Learning & Federated Learning

University of Western Ontario · London, ON, Canada · 2019 – 2025

Developed a fine-tuned LLM using Masked Language Model, Knowledge Distillation, and LoRA, achieving a 7% accuracy improvement in federated multi-task learning.
Designed a Clustered Federated Learning approach using autoencoders and weighted aggregation, improving non-IID sentiment analysis accuracy by 30%.
Critiqued evaluation techniques in clustered federated learning, reducing overestimation errors by approximately 3% across image, text, and sensor datasets.
Engineered federated learning pipelines integrating LSTM and Transformer models to address imbalanced dataset distributions.
Designed and deployed Transformer-CNN architectures for upper and lower body movement detection, improving accuracy from 69.6% to 92.4% with personalization.
Developed personalized CNN architectures with signal processing, improving accuracy from 85.1% to 91.2% in human activity recognition.
Leveraged transfer learning and wavelet decomposition to optimize model performance across diverse datasets.

Part-time Faculty Member — Advanced ML & Generative AI

Fanshawe College · London, ON, Canada · 2024 – 2025

Designed and taught advanced ML and deep learning courses covering LSTM, Transformers, and state-of-the-art LLMs with a focus on Generative AI (GenAI) techniques.
Delivered hands-on training in developing and fine-tuning deep learning models for machine translation, sentiment analysis, and named entity recognition (NER).
Applied techniques such as LoRA, QLoRA, Retrieval-Augmented Generation (RAG), LangChain ecosystem, and GenAI approaches for building scalable and secure AI systems.
Developed project-based learning modules simulating real-world scenarios, enabling students to implement and optimize ML and GenAI workflows from preprocessing to deployment.

Software Developer — Cryptocurrency Trading Systems

Algobot · April – August 2022

Developed a scanner for the Cryptocurrency market to filter cryptos with high momentum signals.
Built a bot to automatically draw static and dynamic supports and resistances and predict future prices.
Examined various strategies with backtests and real-time tests to identify optimal trading strategies.

Data Scientist (Internship — Mitacs)

Xlscout · Mississauga, ON, Canada (Remote) · September – December 2021

Developed ML algorithms to extract solutions and key sections from U.S. patents using BERT and Pegasus.
Engineered data preprocessing and feature extraction pipelines for robust patent document analysis.
Collaborated with cross-functional teams to optimize ML algorithms for patent summarization accuracy and scalability.

Senior Data Analyst (ML Focus)

Royan Institute · Tehran, Iran · January – June 2017

Applied classification models (C4.5 decision tree, logistic regression, radial basis function neural networks, and feed-forward neural networks) to predict umbilical cord blood stem cell freezing decisions.
Predicted quality of cord blood for newborns using ensemble supervised and unsupervised machine learning techniques — resulting in a $1 million cost saving.
Optimized ML workflows through feature engineering and hyperparameter tuning.

Senior Data Analyst — CRM & Recommender Systems

Sina Bank · Iran · November 2016 – February 2017

Extracted strong and frequent association rules between the bank’s web services to improve the recommender system in the Customer Relationship Management (CRM) section.
Used data mining techniques, in particular association rules, to create and improve recommendations for the banking industry.

Data Analyst — Traffic Violation Prediction

Traffic Department of Mazandaran Province · Iran · July – November 2016

Identified factors affecting traffic violations (gender, age, education level, city, vehicle type, driver experience, eyewear) using Logistic Regression (LR), evaluated with the Hosmer-Lemeshow test and McFadden’s pseudo R² index.
Predicted potentially offending drivers using a novel hybrid classification approach: Multi-Layered Perceptron Neural Network trained by Genetic Algorithm and Levenberg–Marquardt algorithm.

Data Analyst — Customer Churn Prediction

Saderat Bank of Iran · February – May 2014

Predicted customer churn using Machine Learning techniques including Support Vector Machine (SVM), Naive Bayes, and Neural Networks.
Predicted multi-level customer churn using Neural Networks, identifying customers prone to churn before completely leaving the organization.
Estimated the loss of Saderat Bank due to customer churn using supervised ML techniques and a proposed custom loss function.

Education

Degree	Institution	Year
Ph.D. in Software Engineering	University of Western Ontario, London, Canada	2021 – 2025
M.Sc. in Software Engineering	University of Western Ontario, London, Canada	2019 – 2021
M.Sc. in Industrial Engineering (Productivity & System Management)	Amirkabir University of Technology (Tehran Polytechnic), Iran	2012 – 2014
B.Sc. in Applied Mathematics (Operations Research)	Ferdowsi University, Mashhad, Iran	2007 – 2012

Technical Skills

AI & Machine Learning

Generative AI · Large Language Models (LLMs) · LLMOps · Agentic AI · AI Agents · RAG (Retrieval-Augmented Generation) · Prompt Engineering · Fine-tuning · LoRA · QLoRA · Federated Learning · Transfer Learning · Deep Learning · NLP · Model Evaluation · Knowledge Distillation

Frameworks & Libraries

PyTorch · TensorFlow · Keras · Scikit-Learn · SciPy · LangChain · LangGraph · LlamaIndex · Pandas · NumPy · Matplotlib

Languages & Databases

Python · SQL · PostgreSQL · Vector Databases · Git

Infrastructure & DevOps

Docker · Kubernetes

Data Visualization

Tableau · Matplotlib

Product & Leadership

AI Product Management · AI Strategy · Product Roadmapping · Agile Delivery · Product Discovery · Value Realization · AI Governance · Responsible AI · Executive Stakeholder Management · Cross-functional Leadership · Innovation Management · Workforce Enablement

Core Competencies

AI Product Management & Strategy
Product Roadmapping & Agile Delivery
AI Governance & Responsible AI
Executive Stakeholder Management
Cross-functional Leadership
Innovation Management
Workforce Enablement & AI Education
Research & Publications

Research & Publications

Journal Articles

“Personalized Models for Human Activity Recognition with Wearable Sensors: Deep Neural Networks and Signal Processing”
Springer Applied Intelligence
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“Deep Neural Networks for Human Activity Recognition with Wearable Sensors: Leave-one-subject-out Cross-validation for Model Selection”
IEEE Access
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“A Case Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting”
Journal of Business-to-Business Marketing
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“Productivity Change and its Determinants: Application of the Malmquist Index with Bootstrapping in Iranian Steam Power Plants”
Utilities Policy
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“Dynamic Changes in CO₂ Emission Performance of Different Types of Iranian Fossil-Fuel Power Plants”
Energy Economics
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Preprints & arXiv

“Gene Selection from Microarray Expression Data: A Multi-objective PSO with Adaptive K-nearest Neighborhood”
arXiv
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Conference Papers

“Investigating the Performance of Technical Indicators in Electrical Industry in Tehran’s Stock Exchange Using Hybrid Methods of SRA, PCA and Neural Networks”
Conference on Thermal Power Plants
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“Customer Churn Prediction Using a New Criterion and Data Mining; A Case Study of Iranian Banking Industry”
International Conference on Industrial Engineering and Operations Management (IEOM)
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“Customer Churn Prediction Using a Meta-Classifier Approach; A Case Study of Iranian Banking Industry”
International Conference on Industrial Engineering and Operations Management (IEOM)
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“Assessing Productivity Changes Using the Bootstrapped Malmquist Index: The Case Study of the Iranian Construction Industry”
International Conference on Industrial Engineering and Operations Management (IEOM)
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20+ peer-reviewed publications · 480+ academic citations
Full publication list: Google Scholar

Soft Skills

Problem-Solving
Effective Communication
Teamwork & Collaboration
Time Management
Executive Presentation & Facilitation

Contact

Channel	Details
Email	dgholamian@gmail.com
LinkedIn	linkedin.com/in/davoud-gholamiangonabadi
GitHub	github.com/dgholamian
Website	dgholamian.github.io
Google Scholar	Scholar Profile