Staff Data Scientist / Technical Lead (Full-Stack / Production ML)

免费注册后解锁"你的匹配分 vs 高分画像"与个人技能缺口。

About Appriss Retail Appriss Retail is the total retail loss solution for omnichannel, unifying high-quality data across stores, online, and customer ser-vice to reduce returns, cut shrink, and manage incidents. Our products— Engage to reduce returns, Secure to cut shrink, and Incident to centralize visibility—help retailers move from reactive loss control to strategic profit protection. Together, they empower organizations to make better operations decisions, strengthen accountability, and put hundreds of millions back to the bottom line . Covering 40% of all U.S. transactions and active in 45 countries , Appriss Retail is trusted by 60+ of the top 100 U.S. retailers to deliver lasting performance improvement. Learn more at apprissretail.com. Overview We are looking for a player-coach who leads a small, high-output data science team while staying deeply hands-on. This role owns the full scope of data science at Appriss Retail — data engineering, governance, and production model delivery — not just model building. The right candidate has built and shipped realdataplatforms andAI/ML systems using a modern stack, has meaningful experience with LLMs and agentic architectures, and canoperatecredibly in both the technical weeds and the business conversation. As a Staff Data Scientist / Technical Lead (Full-Stack / Production ML), you would set technical direction for the team and have the opportunity to grow into direct management over time. This is a full-stack role, not a model-building role with a handoff. If your last few projects ended when you handed a notebook or a trained model to a data engineering or ML engineering team to put into production, this probably isn’t the right fit. Essential Du ties Technical leadership delivery Own end-to-end delivery of high-impact data science projects — from ambiguous businessrequestto production-readysystem. Design andmaintaindata pipelines, data models, and governance standards alongside your team; treat infrastructure as a first-class product concern. Build, evaluate, and iterate on ML models in production; lead experimentation rigor, monitoring, and lifecycle management. Architect and ship LLM-integrated features and agentic workflows — including prompt engineering,tool use, and output evaluation. Guidecloud infrastructurearchitecturefordata scienceprojects, taking into accountperformance,maintenance, and cost criteria. Set the standard for code quality: write production-grade Python and SQL, enforce reviewpractices, andmaintaindocumentation. Partner closely with engineering to integrate models and pipelines into core product infrastructure. Strategy stakeholders Translate ambiguous business problems into well-scoped analytical and modeling work with defined success criteria. Partner with product, engineering, and business stakeholders to ensure data work is grounded in real source systems and product context — not isolated analysis. Contribute to the data and analytics roadmap, balancing near-term delivery with longer-term platform investment. Communicate clearly to non-technical audiences; influence decisions with data and model outputs. Required Qualifications Experience 6+ years of experience in data science, data engineering, or a closely related technical discipline. 1+yearof formal technical lead experience over a team with the readiness and interest to grow into direct management. Technical skills — required Expert-level SQL and Python; production code, not just analysis scripts. Deep understanding of data infrastructure: pipelines, warehousing, data modeling, and source system behavior. Strong software engineering practices: version control, code review, testing, and has built or maintained a CI/CD pipeline for a data or ML workload.. Ability to scope and deliver complex analytical projects independently from vague inputs. Cloud data platform experience: Snowflake, Azure (preferred), AWS, or GCP. Working knowledge of infrastructure-as-code (Terraform, CloudFormation, or equiv

关于Appriss Retail Appriss Retail是全渠道零售损失管理的整体解决方案，统一整合门店、线上和客户服务中的高质量数据，以减少退货、降低损耗并管理事件。我们的产品——Engage用于减少退货，Secure用于降低损耗，Incident用于集中化可视性——帮助零售商从被动损失控制转向战略性利润保护。这些产品共同赋能组织做出更好的运营决策、加强问责制，并将数亿美元返还至底线利润。Appriss Retail覆盖美国所有交易的40%，活跃于45个国家，受到美国前100强零售商中60多家的信赖，以实现持久的绩效提升。了解更多请访问apprissretail.com。概述 我们正在寻找一位球员兼教练式的领导者，能够带领一支小型、高产出的数据科学团队，同时保持深度亲力亲为。该角色负责Appriss Retail数据科学的全部范围——数据工程、治理和生产模型交付——而不仅仅是模型构建。合适的候选人曾使用现代技术栈构建并交付真实的数据平台和AI/ML系统，在LLM和代理架构方面有丰富经验，并能在技术细节和业务对话中都能可信地运作。作为Staff Data Scientist / Technical Lead（全栈/生产ML），您将为团队设定技术方向，并有机会逐步成长为直接管理者。这是一个全栈角色，而不是一个将模型构建移交给他人的角色。如果您最近几个项目在将笔记本或训练好的模型交给数据工程或ML工程团队投入生产时就结束了，那么这可能不适合您。基本职责 技术领导与交付 负责高影响力数据科学项目的端到端交付——从模糊的业务需求到生产就绪系统。与团队一起设计和维护数据管道、数据模型和治理标准；将基础设施视为一等产品关注点。在生产中构建、评估和迭代ML模型；领导实验严谨性、监控和生命周期管理。架构并交付LLM集成功能和代理工作流——包括提示工程、工具使用和输出评估。指导数据科学项目的云基础设施架构，考虑性能、维护和成本标准。设定代码质量标准：编写生产级Python和SQL，执行审查实践，并维护文档。与工程团队紧密合作，将模型和管道集成到核心产品基础设施中。战略与利益相关者 将模糊的业务问题转化为范围明确的分析和建模工作，并定义成功标准。与产品、工程和业务利益相关者合作，确保数据工作基于真实源系统和产品上下文——而非孤立分析。为数据和 analytics 路线图做出贡献，平衡近期交付与长期平台投资。向非技术受众清晰沟通；用数据和模型输出影响决策。必备资格 经验 6年以上数据科学、数据工程或密切相关的技术学科经验。1年以上正式技术领导经验，管理团队，并具备成长为直接管理者的准备和兴趣。技术技能——必备 专家级SQL和Python；生产代码，而不仅仅是分析脚本。深入理解数据基础设施：管道、数据仓库、数据建模和源系统行为。强大的软件工程实践：版本控制、代码审查、测试，并曾为数据或ML工作负载构建或维护CI/CD管道。能够从模糊输入中独立界定并交付复杂分析项目。云数据平台经验：Snowflake、Azure（优先）、AWS或GCP。基础设施即代码（Terraform、CloudFormation或同等工具）的工作知识。

⚑ 岗位信息收录自公开招聘页（Himalayas），以雇主官网为准