We are seeking an engineering-focused Data Scientist to build, operationalize, and maintain production-grade retail forecasting and optimization models. In this role, model development goes hand-in-hand with operational reliability: success is measured by accurate forecasts and stable, low-latency, deterministic pipelines running natively on the Databricks Lakehouse.

You will bridge the gap between applied data science and machine learning engineering. Working in close partnership with Product and Data Engineering, you will own the operational lifecycle of item-store level demand forecasts and downstream replenishment/optimization engines—from distributed feature pipelines to automated Databricks workflows, model tracking, and runtime monitoring.

• Production Forecasting & Optimization: Develop, calibrate, and tune time-series forecasting engines and downstream supply chain/inventory optimization logic using tree-based ensembles (LightGBM, CatBoost) and distributed Python/PySpark.

• Databricks-Native MLOps: Build, schedule, and maintain automated training and batch inference pipelines using Databricks Workflows and Jobs. Leverage MLflow for robust experiment tracking, artifact logging, and model registry management.

   

• Cross-Functional Partnership:

  • With Product: Translate business requirements into technical specs, define operational SLAs, and provide technical feasibility assessments for new forecasting features.

  • With Data Engineering: Establish strict data contracts, define schema validations, and optimize data ingestion/consumption patterns from upstream Delta tables.

     

• Pipeline Quality & Stability: Treat ML pipelines as critical production software. Implement pre-inference data validation gates (e.g., schema checks, missingness thresholds, null checks) and automated alerting to prevent corrupted data from reaching scoring jobs.

• Model & Pipeline Observability: Track pipeline health, monitor runtime performance, and detect feature drift, target drift, and forecast degradation across high-cardinality retail catalogs.

• Software Excellence: Write modular, maintainable, and testable code. Champion version control best practices, unit/integration testing with pytest, and automated CI/CD checks within Git.

• Spec-Driven Execution: Embrace a Spec-Driven Development (SDD) mindset, leveraging modern agentic AI development workflows (e.g., Cursor, Claude Code) to move rapidly from research to reliable production code.

   

• Tree-Based Ensembles: Hands-on experience developing, tuning, and deploying gradient boosted decision trees—specifically LightGBM and CatBoost—on high-cardinality, tabular, and time-series datasets.

• Time-Series Retail Forecasting: Deep practical understanding of demand forecasting challenges: trend, seasonality, calendar events, promotional uplifts, stockouts, and intermittent/sparse demand patterns.

• Databricks Platform: Proven experience building within the Databricks ecosystem, specifically authoring and managing multi-task Databricks Workflows/Jobs, navigating Delta Lake, and using MLflow across the model lifecycle.

• Data Manipulation & PySpark: Strong proficiency in Python and PySpark for distributed data processing, feature engineering, and memory-conscious transformations across massive retail datasets.

• Software Engineering Fundamentals: Solid understanding of clean code principles, modular package design, virtual environments, automated testing (pytest), and standard Git workflows (pull requests, branching, code reviews).

   

• Optimization & Supply Chain: Familiarity with inventory optimization mechanics (safety stock calculation, reorder point modeling, lead time variability, allocation constraints).

• Databricks Advanced Features: Experience leveraging Delta Live Tables (DLT), Unity Catalog for data and model governance, or Photon compute engine.

• Explainable AI (XAI): Experience implementing TreeSHAP or similar interpretability methods within production batch scoring jobs.

• Continuous Integration: Experience setting up or integrating with CI/CD pipelines (e.g., GitHub Actions) to automate testing and deployment into Databricks workspaces.

   

• Production Mindset: You believe a model is only finished when it is tested, automated, monitored, and running reliably in production.

• Ownership & Root-Cause Thinking: When a pipeline fails or a metric degrades, you dig into the logs, identify the root cause, write a regression test, and implement a durable fix.

• Collaborative Communicator: You easily speak the language of business trade-offs with Product managers and system architecture with Data Engineers.

The estimated pay ranges for this role are as follows:

• $120,000 - 140,000 CAD

The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location.