Canada Channel Sales Data Scientist

Imagine what you could do here. The people here at Apple dont just create products they create the kind of wonder thats revolutionized entire industries. Its the diversity of those people and their ideas that inspires the innovation that runs through everything we do from amazing technology to industry-leading environmental efforts. Join Apple and help us leave the world better than we found it!nnApples Channel Sales organization works with carriers resellers and retail partners to bring our products to customers 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 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.n

- 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.n- Architect and own multi-stage data pipelines on modern data warehouses using advanced SQL.n- 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.n- 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.n- Design and deploy statistical and causal-inference methods to simulate potential outcomes such as price elasticity affordability or demand-signal decomposition surfacing actionable recommendations.n- 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.n- Define and implement robust validation strategies to ensure model accuracy reliability and generalizability leveraging both quantitative metrics and qualitative insights.n- 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.n- Collaborate with data engineering teams to build and maintain robust data pipelines and partner with engineering teams to productionalize models and solutions. n- 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.n- Keep up-to-date with the latest industry trends and technologies to ensure work remains cutting-edge and propose continuous improvement of AI platforms.

3 Years of experience in Data Science Analytics Engineering ML or Data Translation roles with a proven track record of delivering impact in industry in a quantitative field (e.g. Computer Science Data Science Statistics Mathematics or related field) or equivalent professional 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).nDemonstrated 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 SQL on a modern cloud warehouse (Snowflake Databricks SQL etc.): advanced WINDOW functions point-in-time joins and defensive grain / NULL layer and metric contracts curated views reusable KPIs metric definitions data contracts between producers and consumers and headless-BI / metrics-layer 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 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.

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).nBash / shell fluency for pipeline tooling deploy scripts and continuous integration and deployment -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).nData-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.