Amazon Data Scientist – ADE Analytics (Canada)

Opportunity Overview

Team Framework & Core Responsibilities

The Alexa Daily Essentials (ADE) Analytics team builds data solutions, automated statistical models, and measurement frameworks for high-frequency Alexa experiences:

[SQL / Pipeline ETL Creation] ➔ [Statistical & ML Modeling] ➔ [Causal Analysis & Root Cause Diagnosis] ➔ [Stakeholder Visualizations]

Core Responsibilities

• Data Infrastructure & Pipeline Development: Write complex SQL queries and ETL pipelines to extract, clean, and combine datasets across Alexa and Amazon services.

• Statistical Modeling & Anomaly Analysis: Develop ML/statistical modeling approaches to identify customer interaction patterns and diagnose root causes of system anomalies.

• Product Insights & Collaboration: Partner with product managers and engineers to define experiment metrics and translate data insights into product decisions.

• Scalable Data Assets & Dashboards: Create automated dashboards and visualization assets to communicate causal metrics to technical and business leadership.

Candidate Eligibility & Skills Matrix

Basic Qualifications

• Educational Background: Master’s degree in a STEM discipline (Science, Technology, Engineering, or Mathematics) or equivalent practical STEM experience.

• Professional Experience: 2+ years of full-time experience as a Data Scientist or in an equivalent quantitative analytics role.

• Querying & Scripting: 2+ years of experience with data querying languages (SQL) and scripting languages (Python, R, SAS, or MATLAB).

• Statistical/ML Modelling: 2+ years of experience applying statistical modelling, machine learning techniques, and evaluating model tuning parameters.

Preferred Technical Stack & Experience

Selection Workflow & Evaluation Stages

[Online Application Submission] ➔ [Recruiter Phone Screening] ➔ [Technical Assessment / Technical Screen] ➔ [Full Loop Interviews]

1. Application Review: Recruiter evaluation of resume, data science project history, SQL proficiency, and STEM educational background.

2. Initial Technical Screen: Focuses on live SQL query building, Python data manipulation, statistical foundations, and basic ML algorithms.

3. Full Interview Loop (4–5 Rounds):

   • Coding & Data Prep: Live SQL and Python/Pandas coding session.

   • Machine Learning & System Design: Designing end-to-end data pipelines, model building, and metric definitions for Alexa product scenarios.

   • Business Acumen & Causal Analysis: Problem-solving session on product experiment evaluation and root-cause analysis.

   • Amazon Leadership Principles: Scenario-based behavioural questions structured via the STAR method (Customer Obsession, Ownership, Dive Deep, Invent and Simplify).