Data Scientist

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.