Data Science & Analytics Student Position

The people here at Apple dont just create productsthey 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! nnAs a Data Science u0026 Analytics - Student Position in Apples Sales organization youll 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. nnPlease Note: This is a Limited Term Employment position (8-Month Co-op) from January to August 2027

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:nn1. Data Pipeline Modernization and Observabilityn- Upgrade and modernize mature production ETL/ELT pipelines that span multiple platforms to ensure stability and reliability.n- Build and maintain automated batch pipelines that ingest clean and transform multi-source data into PostgreSQL and Snowflake environments.n- Stand up pipeline monitoring and observability: freshness checks volume and schema drift detection data quality assertions.n- Optimize database schemas warehouse models and query performance for analytical and AI 2. Workflow Orchestration and Tooling Migrationn- Migrate scheduled jobs and legacy workflows into Apache Airflow and Apples internal orchestration platforms with a focus on avoiding silent failures during and after cutover.n- Document existing job dependencies and build validation and parallel-run strategies to confirm parity before decommissioning.n- Contribute to a catalog of modern Canada tooling and 3. AI Agent Development and Evaluationn- Architect and build agentic workflows Retrieval-Augmented Generation (RAG) systems and custom LLM applications against internal knowledge sources.n- Support the Canada AI Knowledge Hub and Skills Marketplace: knowledge base design content structuring retrieval quality and the user-facing experience.n- Establish rigorous evaluation frameworks for AI systems including ground-truth sets benchmark suites and methods for detecting confidently incorrect answers and hallucinations.n- Build AI agents for cross-source data reconciliation and monitoring resolving discrepancies where multiple systems of record disagree.n- Implement structured logging caching and fallback mechanisms so AI features behave predictably in 4. Analytics Modeling and Tieringn- Conduct exploratory data analysis feature engineering and statistical modeling to uncover actionable insights.n- 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.n- Build robust validation approaches and be prepared to explain and defend individual model outputs to business stakeholders as needed.n- Deliver reporting and visualization that makes model results usable including Tableau dashboards and prepared 5. Data Governance and Enablementn- Contribute to data catalog and metadata efforts including data lineage tracking and documentation of data requirements across sources.n- Curate and prepare datasets for business teams adopting AI tools translating qualitative stakeholder needs into concrete metrics and data products.n- Support AI enablement activities such as office hours documentation and internal 6. Collaboration and Cross-Functional Impactn- Partner with business partners peers and ISu0026T to translate ambiguous problems into concrete data and AI deliverables.n- Write clean well-documented tested code; participate in code reviews; and present project milestones to both technical and executive stakeholders.

Enrolled in a Bachelors or Masters program in Computer Science Data Science Statistics Mathematics Engineering or related quantitative field returning to studies after positions proficiency in Python and standard data libraries (pandas NumPy).nStrong command of SQL and relational database concepts including experience with PostgreSQL u0026 Snowflake or a comparable relational -on experience building end-to-end data pipelines or applications through coursework personal projects hackathons or prior with Git and collaborative version control understanding of algorithms data structures and software engineering AI literacy: familiarity with how LLMs work their failure modes and where they are and are not communication and writing skills critical to working across multiple teams and to juggle multiple responsibilities independently and the judgment to ask questions early when something is unclear.

Candidates are expected to have familiarity and/or experience in these areas although deep expertise in all is not Engineering and OrchestrationnApache Airflow (or Prefect Dagster) for workflow orchestrationnPostgreSQL Snowflake dbtnPipeline testing data quality frameworks (Great Expectations or similar) and monitoring or alerting toolingnData lineage metadata management or data catalog toolsnnAI and LLM Application DevelopmentnLLM and agent frameworks: LangChain LlamaIndex or native tool-use and agent frameworksnRAG architecture prompt engineering and retrieval evaluationnAgent evaluation and benchmarking including LLM-as-judge methods and hallucination detectionnVector search: pgvector Chroma Qdrant or similarnFastAPI Docker and asynchronous PythonnnData Science and AnalyticsnStatistical analysis hypothesis testing and feature engineeringnscikit-learn XGBoost or LightGBM for classification and scoring problemsnComposite scoring weighting and segmentation methodologynTableau and Tableau Prep or comparable BI toolingnnSoftware PracticesnCI/CD pipelines containerization and unit or integration testingnClear technical documentation