# Data Scientist at Upshop

Data Scientist at Upshop in Mississauga. Remote, paying $120k–$140k. Posted October 9, 2026.

- Company: Upshop
- Location: Mississauga
- Workplace: Remote
- Pay: $120k–$140k
- Posted: 2026-10-09
- Apply: https://job-boards.greenhouse.io/upshop/jobs/5262131007
- Source: https://jobmagic.ai/jobs/data-scientist-upshop-yx9mt1

## Requirements

- 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.

## Description

 

### 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.

- **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).

 

### 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.