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Added 7 mins ago
Senior Manager, Credits & Incentives
BDO
AB · Winnipeg, MB · Guelph, ON · London, ON · Toronto, ON · Oakville, ON · Kitchener, ON · Vancouver, BC · Halifax, NS · Calgary, AB
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You have an engineering, science or technology degree and experience in a related field of energy, infrastructure, manufacturing or real estate.
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Apple

Data Scientst - Student Position - Sales Business Analytics

Toronto, ON, Canada

Pay: The base pay range for this role is between $58,900 and $88,600, and your base pay will depend on your skills, qualifications, experience, and location.
Job type: Contract
Apply by: Oct 30, 2026
Posting language: English
Posted: Sep 30, 2026

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Original job description

Summary
The people here at Apple don’t just create products - they create the kind of wonder that’s revolutionized entire industries. It’s 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!

As a Data Scientist - Student Position, Sales Business Analytics in Apple's Sales organization, you'll play a key role in supporting our mission. Collaborating with Sales Professionals, Finance, Operations and Senior Leadership, you will deliver operational excellence by providing data insights, reporting, analytics, and tools to advance our strategic vision. We are searching for a student who can be flexible in the face of business ambiguity and eager to analyze/break down sophisticated datasets to derive clear decisions for our various business partners.

Please Note: This is a Limited Term Employment position (8-Month Co-op) from January to August 2027

Description
This program offers mentorship and development to elevate your business acumen and give visibility to a wide spectrum of business projects at Apple. You will be embedded within a Sales Data and Analytics team and will own deliverables end to end. Depending on team fit, your work will center on a subset of the following:

Data Pipeline Modernization and Observability
Upgrade and modernize mature production ETL/ELT pipelines that span multiple platforms to ensure stability and reliability.
Build and maintain automated batch pipelines that ingest, clean, and transform multi-source data into PostgreSQL and Snowflake environments.
Stand up pipeline monitoring and observability: freshness checks, volume and schema drift detection, data quality assertions.
Optimize database schemas, warehouse models, and query performance for analytical and AI workloads.
Workflow Orchestration and Tooling Migration
Migrate scheduled jobs and legacy workflows into Apache Airflow and Apple's internal orchestration platforms, with a focus on avoiding silent failures during and after cutover.
Document existing job dependencies and build validation and parallel-run strategies to confirm parity before decommissioning.
Contribute to a catalog of modern Canada tooling and automations.
AI Agent Development and Evaluation
Architect and build agentic workflows, Retrieval-Augmented Generation (RAG) systems, and custom LLM applications against internal knowledge sources.
Support the Canada AI Knowledge Hub and Skills Marketplace: knowledge base design, content structuring, retrieval quality, and the user-facing experience.
Establish rigorous evaluation frameworks for AI systems, including ground-truth sets, benchmark suites, and methods for detecting confidently incorrect answers and hallucinations.
Build AI agents for cross-source data reconciliation and monitoring, resolving discrepancies where multiple systems of record disagree.
Implement structured logging, caching, and fallback mechanisms so AI features behave predictably in production.
Analytics, Modeling, and Tiering
Conduct exploratory data analysis, feature engineering, and statistical modeling to uncover actionable insights.
Develop and refine multi-factor scoring and tiering models (Business Tiering, POS Tiering, Carrier Analytics, and related frameworks) that combine several weighted inputs into a single classification.
Build robust validation approaches and be prepared to explain and defend individual model outputs to business stakeholders as needed.
Deliver reporting and visualization that makes model results usable, including Tableau dashboards and prepared datasets.
Data Governance and Enablement
Contribute to data catalog and metadata efforts, including data lineage tracking and documentation of data requirements across sources.
Curate and prepare datasets for business teams adopting AI tools, translating qualitative stakeholder needs into concrete metrics and data products.
Support AI enablement activities such as office hours, documentation, and internal guidance.
Collaboration and Cross-Functional Impact
Partner with business partners, peers, and IS&T to translate ambiguous problems into concrete data and AI deliverables.
Write clean, well-documented, tested code; participate in code reviews; and present project milestones to both technical and executive stakeholders.

Minimum Qualifications

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.
Strong proficiency in Python and standard data libraries (pandas, NumPy).
Strong command of SQL and relational database concepts, including experience with PostgreSQL & Snowflake or a comparable relational database.
Hands-on experience building end-to-end data pipelines or applications through coursework, personal projects, hackathons, or prior internships.
Experience with Git and collaborative version control workflows.
Foundational understanding of algorithms, data structures, and software engineering design.
Demonstrated AI literacy: familiarity with how LLMs work, their failure modes, and where they are and are not appropriate.
Strong communication and writing skills, critical to working across multiple teams and functions.
Flexibility to juggle multiple responsibilities independently, and the judgment to ask questions early when something is unclear.

Preferred Qualifications

Candidates are expected to have familiarity and/or experience in these areas, although deep expertise in all is not required.
Data Engineering and Orchestration
Apache Airflow (or Prefect, Dagster) for workflow orchestration
PostgreSQL, Snowflake, dbt
Pipeline testing, data quality frameworks (Great Expectations or similar), and monitoring or alerting tooling
Data lineage, metadata management, or data catalog tools
AI and LLM Application Development
LLM and agent frameworks: LangChain, LlamaIndex, or native tool-use and agent frameworks
RAG architecture, prompt engineering, and retrieval evaluation
Agent evaluation and benchmarking, including LLM-as-judge methods and hallucination detection
Vector search: pgvector, Chroma, Qdrant, or similar
FastAPI, Docker, and asynchronous Python
Data Science and Analytics
Statistical analysis, hypothesis testing, and feature engineering
scikit-learn, XGBoost, or LightGBM for classification and scoring problems
Composite scoring, weighting, and segmentation methodology
Tableau and Tableau Prep, or comparable BI tooling
Software Practices
CI/CD pipelines, containerization, and unit or integration testing
Clear technical documentation

Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $58,900 and $88,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple Inc.’s discretionary employee stock programs. Employees are eligible for discretionary restricted stock unit award recommendations, and can purchase Apple Inc. stock at a discount if voluntarily participating in Apple Inc.’s Employee Stock Purchase Plan. Participation in Apple Inc.’s discretionary stock programs is governed by Apple Inc.’s stock plans and agreements and are not part of local employment contracts or compensation.

You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit and compensation programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. Apple provides reasonable accommodations to applicants with disabilities. Apple is a drug-free workplace.

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here - in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

This posting is for an existing opportunity at Apple.

Qualified candidates can expect a hiring rate up to the midpoint of the pay range. On an exceptional basis, candidates with significant experience and skills for the role may be paid up to the range maximum.

Role Number: 200686205-3965

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More about this job

Responsibilities

Build and modernize data pipelines, orchestration workflows, and AI applications, while developing analytics, scoring models, and reporting for Apple's Sales organization. Collaborate with business and technical partners to deliver reliable data products, evaluate AI systems, document data governance practices, and communicate project outcomes.

Requirements

Applicants must be enrolled in a bachelor's or master's program in a quantitative field and return to their studies after the eight-month co-op. They need proficiency in Python and SQL, hands-on experience building data pipelines or applications, familiarity with relational databases and Git, foundational software engineering knowledge, AI literacy, and strong communication skills.

Skills

Python
Pandas
NumPy
SQL
PostgreSQL
Snowflake
Data Pipeline Development
Apache Airflow
Large Language Models
Retrieval-Augmented Generation
Statistical Modeling
Feature Engineering
Tableau
Git
Data Quality Monitoring
Communication

Benefits

Medical Coverage
Dental Coverage
Retirement Benefits
Discounted Products
Free Services
Education Expense Reimbursement
Employee Stock Purchase Plan
Restricted Stock Unit Awards
Discretionary Bonuses
Relocation Assistance

Visa sponsorship

Not detected in the job text

Keywords

Python
Pandas
NumPy
SQL
PostgreSQL
Snowflake
Apache Airflow
Prefect
Dagster
Dbt
ETL/ELT
Data Pipelines
Data Observability
Data Quality
Data Lineage
Large Language Models
Agentic Workflows
Retrieval-Augmented Generation
LangChain
LlamaIndex
Vector Search
FastAPI
Docker
Scikit-Learn
XGBoost
LightGBM
Tableau
Git
Statistical Modeling
CI/CD

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