From Upshop's posting. “We” and “our” refer to the employer.
Position Overview
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.
Key Responsibilities
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.
Required Technical Skills
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.
), and standard Git workflows (pull requests, branching, code reviews).
Preferred Qualifications
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.
Cultural Alignment & Values
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.
About this listing
LAYIQ is an independent job-discovery service. This listing does not imply a partnership with or endorsement by the employer. Review the original posting for current details and availability.
