苹果Senior Data Scientist, ASE iCloud Data Organization
任职要求
Minimum Qualifications 7+ years of experience in data analytics or data science, with 5+ years in data science applying statistical modeling, or Machine Learning in production. Proven ability to influence cross‑functional initiatives and shape product or business strategy using data‑informed insights. Strong communicator skilled at framing complex technical concepts for senior and executive audiences. Deep expertise in product analytics, launch reporting, and impact measurement. Proficiency in SQL and at least one programming language (Python, R, etc.), with strong statistical and experimental design foundations. Ability to structure ambiguous problem spaces, prioritize effectively, and deliver…
工作职责
Partner with Product, Engineering, Design, and Business leads to define priorities, shape measurement frameworks, and translate insights into growth strategies for iCloud. Deliver data‑informed narratives for senior leadership that directly inform product and business roadmaps. Drive organizational alignment by clarifying analytical outcomes and implications for diverse stakeholders. Design performance metrics that capture product health, user engagement, and business impact across iCloud services. Lead controlled experiments, apply causal frameworks and marketing mix models to evaluate initiatives at scale. Conduct deep behavioral analysis to uncover engagement, retention, and churn drivers, then translate findings into actionable strategies. Collaborate with Data Engineering to define instrumentation and build reliable data pipelines across ASE. Develop and productionize ML and statistical models that enable predictive and diagnostic insights for product decisions. Leverage generative AI to automate analytical workflows and expand measurement capabilities at scale.
• Collaborate with BIE,DE, PM, CSM to research, design, develop, and evaluate generative AI solutions to address Global Selling challenges. • Interact with stakeholders directly to understand their business problems, aid them in implementation of generative AI solutions, brief stkaholders and guide them on adoption patterns and paths to production • Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
【数据研发/数据分析方向】 1. 负责国际化出行（拉美、亚太、中东等）用户增长、用户运营、广告投放，支持全球多国家/多时区/多币种数据模型。 2. 熟悉数仓架构（ODS→CDM→ADS），确保数据质量、一致性及可扩展性，丰富业务应用层集市，构建业务数据门户，提升数据分析、业务决策的效率。 3. 与增长产品经理、策略运营深度协同，将埋点、用户行为、营销花费、补贴效果等数据沉淀为可复用的增长数据资产。 4. 构建实时+离线数据体系，支持用户画像的实时获取，以及实时计算用户平台价值。 5. 制定并落地数据治理规范（口径、生命周期、成本、权限、合规），满足 GDPR/CCPA/当地数据出境要求。 6. 建立数据异常检测与自动修复机制，保障核心指标（ROI、CAC、留存、LTV）的准确性与及时性。
• Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. • Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). • Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. • Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. • Responsible AI. Produce review artifacts, document mitigations for safety/privacy/fairness, support red‑teaming and sensitive‑use checks, and align with Microsoft’s Responsible AI Standard. • Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists.
AI Agent Engineering • Design, develop, and deploy production-grade AI agent systems, including multi-agent orchestration, tool-use frameworks, memory management, and API integration — ensuring reliability, scalability, and maintainability • Build and optimize Retrieval-Augmented Generation (RAG) pipelines: document ingestion, chunking strategy, embedding, vector search, and re-ranking to maximize LLM grounding quality • Support LLM adaptation to WWGS business domains through prompt engineering, context injection, fine-tuning signal curation, and systematic prompt evaluation frameworks • Develop automated knowledge base construction and real-time data access capabilities (Data Agent, MCP server/client) to connect AI agents with live business data • Design and implement LLM evaluation pipelines to systematically assess agent output quality, hallucination risk, and business impact Data Engineering • Design and implement end-to-end data pipelines (batch and streaming) for data collection, transformation, and storage — supporting both AI application and analytics use cases • Build and maintain integration layer data models that serve as a unified, AI-ready data foundation across WWGS domains • Develop automated data quality monitoring, alerting, and observability tooling to ensure pipeline reliability and data trustworthiness • Integrate multi-source data (seller behavior, transaction logs, off-platform signals, AI outputs) into a coherent, governed data layer • Establish data standardization and governance policies ensuring consistency, accuracy, and compliance across AI and BI consumption layers Technical Leadership • Provide technical guidance on AI-data architecture decisions; define best practices for the team's AI agent and data engineering stack • Collaborate cross-functionally with Product, Operations, and Science teams to translate business requirements into scalable technical solutions • Mentor junior engineers and conduct design reviews; raise the technical bar across the team