Precision Pricing at Scale: AI-Driven Margin Optimization for National Retail
Algorithmic Commerce

Precision Pricing at Scale: AI-Driven Margin Optimization for National Retail

The Business Context

In the low-margin world of grocery retail, promotions drive 40% of revenue. However, the retailer was trapped in a cycle of “repetitive promotion,” applying identical pricing across vastly different demographics. This lack of granularity resulted in significant margin “leakage” and supply chain volatility.

The Strategic Mandate

  • Localized Elasticity: Moving from national pricing to store-cluster optimization based on local demand.
  • Waste Reduction: Aligning promotional pricing with supply chain capacity to reduce the “bullwhip effect.”
  • Merchant Empowerment: Transitioning from manual spreadsheets to an “AI-augmented” planning workflow.

The Strategic Approach

I led the design of a modular, hybrid solution that merged high-volume data engineering with advanced mathematical optimization.

Key Leadership Decisions:

  • Scalable Data Ingestion: Architected a pipeline to process and normalize 24 billion transactional records, ensuring high-fidelity inputs for the forecasting models.
  • Hybrid Modeling (ML + Optimization): Combined Machine Learning for demand forecasting with Mixed-Integer Non-Linear Programming (MINLP) to solve for the optimal balance of price, volume, and margin.
  • Human-in-the-Loop Design: Prioritized the “Merchant UX,” ensuring the AI acted as a co-pilot that experts could review and override, rather than a “black box” that alienated the domain specialists.

What We Delivered

  • Dynamic Store Clustering: Developed algorithms to group 1,000+ stores by price sensitivity rather than just geography, uncovering hidden margin opportunities.
  • Automated Promotion Scheduling: Engineered a scalable engine capable of generating full quarterly schedules while respecting complex business constraints (e.g., inventory limits, vendor contracts).
  • Production-Grade Vertex AI Pipeline: Built an end-to-end MLOps workflow on Google Cloud to handle iterative A/B testing and model retraining at scale.

The Strategic Impact

  • Direct Cash Flow: Delivered an estimated $85,000 CAD per store, per year in free cash flow impact.
  • Margin Expansion: Realized a 4.6% gross margin improvement specifically on unadvertised promotional lines.
  • Operational Efficiency: Freed merchant teams from manual data entry, allowing them to focus on high-level category strategy.
  • Supply Chain Stability: Improved upstream visibility, reducing waste and overstocking by smoothing out promotional demand spikes.

Infrastructure & Science Stack

  • Cloud Platform: Google Cloud (BigQuery, Vertex AI, DataProc)
  • Orchestration: Cloud Composer (Apache Airflow)
  • Optimization: Mixed-Integer Programming & Hierarchical Clustering

Data Scale: 24B+ records across 1,000+ stores

Executive Reflection

This project was a masterclass in Organizational Change Management disguised as a tech project. In retail, data doesn’t matter if the merchants don’t trust the model. By building a transparent, high-performance system that delivered measurable cash flow, we proved that AI isn’t a replacement for human judgment - it’s the most powerful lever a merchant has to drive profitability in a hyper-competitive market.

CROSS-INDUSTRY EXPERTISE

Additional case studies

LEADERSHIP, SCALE, AND THE HUMAN ELEMENT

Principles & Perspectives

How do you define successful engineering leadership?

The Philosophy

Many view technical leadership as being the “smartest architect in the room.” I see it as the opposite. My job is to build a room where I don’t have to be the smartest person because the systems, culture, and communication are so robust that the team can out-innovate me.

The Strategy

  • Alignment: Does every engineer understand how their sprint task impacts the company’s bottom line?
  • Velocity vs. Stability: We aren’t just “shipping fast”; we are building a predictable, repeatable engine that doesn’t collapse under its own weight at the next order of magnitude.
  • The Human Growth Curve: Success is when the engineering team’s capability evolves faster than the product’s complexity. If the team feels stagnant, the tech stack will soon follow.

What is your approach to scaling technical organizations?

The Philosophy

Scaling isn’t just “hiring more people” - that’s often how you slow down. Scaling is about moving from Individual Heroics to Organizational Systems.

The Strategy

  • The 3-Continent Perspective: Having managed global teams, I focus on “High-Signal Communication.” As you grow, the cost of a meeting triples. I implement “Asynchronous-First” cultures that protect deep-work time while ensuring no one is blocked by a timezone.

  • Modular Autonomy: I advocate for breaking down monolithic teams into autonomous units with clear ownership. This reduces the “communication tax” and allows us to scale the headcount without scaling the bureaucracy.

  • Automation as Infrastructure: At petabyte scale, manual intervention is a failure. I treat the developer experience (CI/CD, observability, self-service infra) as a first-class product to keep the “path to production” frictionless.

How do you balance high-growth velocity with technical stability?

The Philosophy

Technical debt isn’t a “bad thing” to be avoided; it’s a set of historical decisions that no longer serve you. Like any loan, leverage can accelerate growth when investments payoff. But if velocity and returns are slowing you need a payment plan before the interest kills you.

The Strategy

  • The ROI Filter: I don’t refactor for the sake of “clean code.” I don’t refactor a micro-service with no users. I refactor when the pain on that debt - measured in bugs, downtime, or developer frustration - starts to exceed the cost of the fix.

  • Zero-Downtime Culture: Especially at scale, stability is a feature. I implement “Guardrail Engineering” where the system is designed to fail gracefully, ensuring that a Series B growth spike becomes a success story rather than a post-mortem.

  • The 70/20/10 Rule: I typically aim to dedicate 70% of resources to new features, 20% to infrastructure/debt, and 10% to R&D. This ensures we never stop innovating, but we never stop fortifying either.