Canada Channel Sales Data Scientist

Apple•Toronto, ON

About The Position

Apple's Channel Sales organization works with carriers, resellers, and retail partners to bring our products to customers worldwide. The Channel Sales team in Canada is hiring a Data Scientist with deep analytics-engineering craft to build and own the semantic layer, curated metrics, and pipelines behind partner planning, demand signals, and pricing — and to act as regional owner of the global data and AI roadmap. This is a hands-on role for a builder pairing data modelling and pipeline craft with statistical and causal-inference depth to answer the hard channel questions, working closely with Sales, Finance, Worldwide (WW) counterparts, and engineering.

Requirements

3+ Years of experience in Data Science, Analytics Engineering, ML, or Data Translation roles, with a proven track record of delivering impact in industry settings.
Degree in a quantitative field (e.g., Computer Science, Data Science, Statistics, Mathematics, or related field), or equivalent professional experience.
Strong Python for analysis and production-grade code, such as pandas or scikit-learn, plus REST API integration and connector libraries (e.g., warehouse connectors, sqlalchemy).
Demonstrated ability to collaborate with distributed engineering or data science teams to deliver business value while building trust with non-technical leaders and translate undefined questions into end-to-end solutions.
Expert SQL on a modern cloud warehouse (Snowflake, Databricks SQL etc.): advanced WINDOW functions, point-in-time joins, and defensive grain / NULL handling.
Semantic layer and metric contracts — curated views, reusable KPIs, metric definitions, data contracts between producers and consumers, and headless-BI / metrics-layer patterns.
Statistical, causal-inference, and time-series fluency — experimental design, instrumental variables (incl. Wald / Intent-to-Treat estimators), A/B and natural experiments; fiscal-calendar alignment, YoY architectures, fill / cascade strategies.
Modern engineering practices — Git version control, docs-as-code discipline, and LLM-assisted tooling (e.g., Claude, Cursor) with prompt engineering, hallucination mitigation, and eval discipline.

Nice To Haves

Hands-on experience with at least one large-scale data platform (e.g., Snowflake, BigQuery) and one pipeline-orchestration / analytics platform (e.g., dbt, Airflow).
Bash / shell fluency for pipeline tooling, deploy scripts, and continuous integration and deployment automation.
Analytics-engineering craft — Kimball-style dimensional design (e.g., star schemas, Slowly-Changing Dimensions); layered transformations (staging → intermediate → marts), idempotent models, Directed Acyclic Graphs (DAGs), unit and data tests, and automated lineage (dbt or equivalent).
Data-product ownership and production-lifecycle awareness — discovery → delivery loops (e.g., Jobs-to-be-done framing, OKRs); model deployment, CI/CD, monitoring, model registries, and Service Level Objectives / Indicators (SLOs / SLIs) for data products.

Responsibilities

Bridge data engineering, data science, and business with the ability to collaborate with business leaders and cross-functional stakeholders to proactively identify business opportunities and translate complex business problems into well-defined technical and analytical requirements.
Architect and own multi-stage data pipelines on modern data warehouses using advanced SQL.
Build single-source-of-truth data layers and a semantic layer with metric and grain contracts for forecasting, pricing, and channel performance, standardising definitions (e.g., sell-through, contribution margin) trusted across Sales, Finance, and channel teams.
Build executive-ready dashboards and self-service tools in pipeline-platform-native BI; enable downstream BI and SQL users through trusted curated data sources and data dictionaries.
Design and deploy statistical and causal-inference methods to simulate potential outcomes such as price elasticity, affordability, or demand-signal decomposition — surfacing actionable recommendations.
Use Large Language Models (LLMs) and agentic tools as a productivity layer such as text-to-SQL or retrieval-augmented querying, with evals to verify correctness.
Define and implement robust validation strategies to ensure model accuracy, reliability, and generalizability, leveraging both quantitative metrics and qualitative insights.
Maintain architecture decision records, design docs, runbooks, and team wiki content; mentor peers through code reviews and modeling standards so the pipelines and their rationale remain transferrable.
Collaborate with data engineering teams to build and maintain robust data pipelines and partner with engineering teams to productionalize models and solutions.
Collaborate with Worldwide (WW) teams to act as the subject matter expert for global AI initiatives and localizing global AI tools and other emerging solutions, ensuring their effectiveness and relevance for the Canadian business.
Keep up-to-date with the latest industry trends and technologies to ensure work remains cutting-edge and propose continuous improvement of AI platforms.