Data Science & Analytics - Student Position

Apple Canada is looking for a curious and driven student to join its Sales Data and Analytics team in Toronto, Ontario as a Data Science & Analytics Student (Co-op) . This is an 8-month Limited Term Employment position running from January to August 2027 — an opportunity to embed yourself within one of the world's most influential technology companies and contribute to real, end-to-end data and AI deliverables.

This isn't a passive placement. You'll collaborate directly with Sales Professionals, Finance, Operations, and Senior Leadership to deliver operational excellence through data insights, reporting, analytics, and tooling . Whether you're modernizing data pipelines, building AI agents, or developing scoring models, the work you do here will have a direct impact on Apple's strategic vision in Canada.

About the Role: Data Science & Analytics Student Position

Embedded within the Sales Data and Analytics team, you'll own deliverables from start to finish. Your work will span several interconnected areas depending on team fit — from data pipeline modernization and ETL/ELT development in PostgreSQL and Snowflake , to workflow orchestration with Apache Airflow , to building and evaluating AI agents and Retrieval-Augmented Generation (RAG) systems . You'll also support analytics and tiering models (including Business Tiering, POS Tiering, and Carrier Analytics), create Tableau dashboards , and contribute to data governance and metadata efforts .

The program is designed to provide mentorship and professional development , giving you visibility across a wide spectrum of business projects. You'll write clean, well‑documented code, participate in code reviews , and present project milestones to both technical teams and executive stakeholders. Strong communication skills and independent judgment are just as valued here as technical depth.

Benefits and Salary

The base pay range for this co‑op role is between $58,900 and $88,600 , with your specific rate depending on skills, qualifications, experience, and location. Apple's total compensation package also includes access to comprehensive medical and dental coverage , retirement benefits , discounts on Apple products and services, and tuition reimbursement for formal education related to career advancement. Employees may also be eligible to participate in Apple's Employee Stock Purchase Plan and discretionary stock programs. Relocation support and discretionary bonuses or commission payments may also apply to this role.

Responsibilities

Day‑to‑day work in this role touches everything from maintaining production data pipelines to developing cutting‑edge AI applications . You'll be expected to take full ownership of your deliverables, communicate proactively, and adapt as business priorities evolve. Here's a breakdown of the key areas you'll contribute to:

• Upgrade and modernize mature ETL/ELT production pipelines spanning multiple platforms to ensure stability, reliability, and analytical performance
• Build and maintain automated batch pipelines that ingest, clean, and transform multi‑source data into PostgreSQL and Snowflake environments
• Implement pipeline monitoring including freshness checks, schema drift detection, and data quality assertions
• Migrate scheduled jobs and legacy workflows into Apache Airflow and Apple's internal orchestration platforms, with robust validation and parallel‑run strategies
• Architect and build agentic workflows, RAG systems, and custom LLM applications against internal knowledge sources, including support for the Canada AI Knowledge Hub
• Establish evaluation frameworks for AI systems, including ground‑truth sets, benchmark suites, and hallucination detection methods
• Develop and refine multi‑factor scoring and tiering models (Business Tiering, POS Tiering, Carrier Analytics) combining weighted inputs into single classifications
• Deliver reporting and visualizations through Tableau dashboards and prepared datasets that make model results usable for business stakeholders
• Contribute to data governance efforts including data lineage tracking, metadata management, and documentation of data requirements across sources
• Collaborate cross‑functionally with business partners and IS&T, translating ambiguous problems into concrete data and AI deliverables, and presenting milestones to technical and executive audiences

Requirements / Skills

Apple is looking for a student who thrives in ambiguity and can move fluidly between data engineering, analytics, and AI application development . The ideal candidate is currently enrolled in a quantitative program, is comfortable owning work independently, and knows when to ask for guidance early. Strong technical fundamentals combined with clear communication are essential to succeeding in this role.

• Enrolment in a Bachelor's or Master's program in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field, with a return to studies following the co‑op term
• Strong proficiency in Python and standard data libraries including pandas and NumPy
• Strong command of SQL and relational database concepts, with hands‑on experience in PostgreSQL, Snowflake, or a comparable platform
• End‑to‑end data pipeline or application experience through coursework, personal projects, hackathons, or prior internships
• Experience with Git and collaborative version control workflows
• Foundational knowledge of algorithms , data structures, and software engineering design principles
• Demonstrated AI literacy — familiarity with how LLMs function, their failure modes, and appropriate use cases
• Strong written and verbal communication skills , with the ability to work effectively across multiple teams and functions
• Preferred (not required): Apache Airflow, dbt, LangChain, LlamaIndex, RAG architecture, scikit‑learn, XGBoost, Tableau, FastAPI, Docker, CI/CD pipelines, and vector search tools such as pgvector or Chroma

Enrolled in a Bachelor's or Master's program in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field, returning to studies after position's term.

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