hadi@hadiaghazadeh.com · Calgary, Alberta, Canada
Profile
Senior Data Scientist and applied machine learning researcher with 8+ years building production forecasting, optimization and decision-intelligence systems. I own the end-to-end development and operation of locational marginal price forecasting across 10,000+ pricing nodes in US and Canadian electricity markets, covering feature engineering, applied research, model development, distributed training, deployment, APIs, monitoring and production maintenance.
Specialized in time-series forecasting, operations research, mixed-integer optimization and reinforcement learning, with hands-on experience building cloud-native ML systems. PhD candidate, published researcher, and author of a practical book on applied reinforcement learning.
Experience
Senior Data Scientist — Enverus
March 2026 – Present · Calgary, Canada
- Own end-to-end development and production operation of locational marginal price forecasting covering more than 10,000 pricing nodes across U.S. and Canadian electricity markets.
- Lead the full ML lifecycle: data analysis, feature engineering, applied research, model development, backtesting, distributed training, deployment, API development, monitoring, incident resolution and ongoing maintenance.
- Build and operate large-scale training and inference pipelines on Kubernetes, with Google Cloud for model serving and storage, Snowflake for analytical workloads and Kafka for streaming data.
- Develop reproducible deployment and model-management workflows with Pulumi and Terraform; production observability with Grafana and Sentry.
- Research and prototype large-scale mixed-integer linear programming for power-flow and security-constrained unit commitment, emphasising fast, computationally efficient electricity-price optimization.
- Investigate reinforcement-learning-guided cutting-plane selection to improve branch-and-bound search and accelerate large-scale power-system optimization models.
Machine Learning Engineer and Technical Lead — Bits in Glass
April 2024 – February 2026 · Calgary, Canada (Remote)
- Led technical development of a production hierarchical RAG platform processing more than 20 million oil-and-gas documents, combining structured and unstructured data, hybrid retrieval, embedding-model evaluation, Databricks, LangChain and Model Context Protocol.
- Architected a production dynamic fuel-pricing optimization system using integer linear programming and contextual bandits, supporting adaptive margin- and volume-based strategies and cutting pricing decision time from several hours to under 30 minutes.
- Directed implementation of an agentic AI framework for railway legacy-system modernization, automating extraction of business rules and user stories from COBOL applications.
- Built an AI-enabled energy-sector digital twin integrating AWS TwinMaker, IoT sensor data, 3D asset models and natural-language scenario exploration.
- Developed an end-to-end LLM document-processing pipeline with LangGraph and Databricks Mosaic AI, achieving 95%+ structured extraction accuracy on batch invoice data.
- Designed a repeatable evaluation framework for enterprise RAG systems with limited ground-truth data.
Machine Learning Developer — AltaML
May 2023 – September 2023 · Calgary, Canada (Hybrid)
- Developed a scalable hybrid demand-forecasting system for 100+ fuel-retail locations, improving inventory planning and generating more than $1 million in projected annual savings.
Senior Data Scientist — Snapp
May 2021 – June 2022 · Tehran, Iran (Hybrid) Largest ride-hailing platform in the Middle East
- Led development of a reinforcement-learning-based dynamic surge-pricing system that improved order-fulfilment rate by 5% across more than 200,000 daily orders, improving marketplace balance, revenue performance and driver utilization.
- Directed development of an unsupervised fraud-detection system achieving 90%+ recall and eliminating more than 300 hours of manual review per month.
Data Scientist — Fanap (PARSA)
January 2018 – April 2021 · Tehran, Iran
- Led development of an end-to-end ATM cash-demand forecasting and replenishment optimization system, achieving 17% MAPE and reducing operational costs by 10%.
Education
PhD Candidate, Geomatics Engineering — University of Calgary
July 2022 – Defending 9 October 2026 · GPA 4.0/4.0
Research: reinforcement learning, real-time policy optimization, vehicle routing, disaster-response logistics and transportation optimization.
MSc, Industrial Engineering — Amirkabir University of Technology
2015 – 2018 · Tehran Polytechnic
BSc, Industrial Engineering — Iran University of Science and Technology
2011 – 2015
Book and selected publications
Aghazadeh, H. (2026). Applied Reinforcement Learning: Business optimization and LLM fine-tuning. Manning Publications. Publisher
The research page lists all publications with venues and links.
Technical expertise
Forecasting and machine learning — time-series forecasting, demand forecasting, LMP forecasting, feature engineering, backtesting, model evaluation, deep learning, transformer architectures, large language models, RAG, agentic AI, foundation-model fine-tuning
Optimization and decision science — operations research, mixed-integer linear programming, security-constrained unit commitment, power-flow optimization, branch-and-bound, cutting planes, reinforcement learning, deep RL, contextual bandits, dynamic pricing, vehicle routing, supply-chain optimization
Production ML and infrastructure — Kubernetes, Google Cloud Platform, Snowflake, Kafka, Terraform, Pulumi, Grafana, Sentry, Databricks, MLflow, Docker, AWS, SageMaker, Bedrock, Microsoft Azure, CI/CD for ML, model serving, API development, production monitoring
Frameworks and tools — Python, PyTorch, TensorFlow, LangChain, LangGraph, Hugging Face, Model Context Protocol, OpenAI API, Claude API, Apache Spark
Certifications
- Databricks Certified Machine Learning Associate (2024)
- Reinforcement Learning Specialization, University of Alberta / Coursera (2022)
Selected honors and awards
- Alberta Innovates Graduate Student Scholarship (2024) — $31,000 per year for two years
- First Place, Amii Reinforcement Learning Competition, Upper Bound Conference (2024)
- Open Doctoral Scholarship, University of Calgary (2024) — $15,000
- First Place, AI Demand Forecasting Competition, Amirkabir University of Technology (2018) — first among more than 150 teams
Leadership and teaching
- Vice President and Student Representative, Graduate Geomatics Group, University of Calgary (2023–2024) — led a graduate engineering community of more than 50 members
- Teaching Assistant, University of Calgary — Engineering Design, Programming with Data, Spatial Data Mining
- Course instructor and creator — Contextual Multi-Armed Bandits in Python and Reinforcement Learning for Operations Research
- Peer reviewer for machine learning, transportation, optimization and spatial-computing journals
Languages
English (advanced) · Persian (native)