专业要求：
学历要求：大专及以上
工作经验：不限
薪资待遇：面议
招聘人数：3
招聘对象： 社会人才
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工作地区： 上海 学历要求：本科及以上性别要求：不限
工作经验：5-7 年薪资待遇：面议招聘人数： 3
公司性质：公司规模：所属行业：IT行业-计算机、互联网、通讯、电子、仪器仪..
职位描述：
1、负责iOS项目的技术评估和研发，及现有项目的维护升级2、根据需求和设计带领iOS团队完成开发工作3、负责iOS产品的性能、兼容等调优4、负责制定相关代码规范、iOS项目架构升级、基础库建设及技术文档输出岗位要求：1、计算机或相关专业大学本科以上学历，5年以上iOS开发经验，扎实的计算机专业基础（算法、数据结构,常用的设计模式等)，主导3个以上中等以上规模的iOS项目2、主导设计过成熟的架构方案，对iOS项目模块化，团队协作流程能起到关键性作用3、有能力分析找出iOS应用性能瓶颈，并有各种解决方案4、熟练掌握objectivec，并有大量编码实践5、熟悉UI、多线程、App运行生命周期、内存管理、常见开源库等技术6、掌握AppStore的发布规则和审核准则7、有H5和Native混合开发经验，熟悉移动端跨平台开发技术8、熟练使用Git，有持续集成如fastlane，或其他方案实施经验者优先9、擅长解决问题，具备良好的沟通和抗压能力，有带团队或项目管理经验优先10、熟悉音视频处理相关技术优先 [详情]
我要投递简历
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工作地区： 上海 学历要求：硕士及以上性别要求：不限
工作经验：不限薪资待遇：2-4 月薪招聘人数： 1
公司性质：公司规模：所属行业：IT行业-计算机、互联网、通讯、电子、仪器仪..
职位描述：
Responsibilities Use predictive/statistical modeling and related methods to build world-class, scalable models that will provide high business value. Apply advanced statistics and data mining techniques to analyze and make insights from big data, such as historical production data and simulation/experiment results. Create computer simulations to support operational decision-making. Identify areas with potential for improvement and work with internal teams to generate requirements that can realize these improvements. Create software operational machine learning systems to integrate with commercial software.Basic Qualifications Master’s degree in statistics, mathematics or computer science or minimum 7 years’ equivalent job experience Minimum 7 years’ experience in one or more of the following:o statistical programming language (preferably Python, Scala, R or MATLAB/Octave),o data management (SQL, ETL , Data Factory, data warehouse, etc.) and using databases in a business environment with large-scale, complex datasets, o developing predictive/machine learning models. Strong English verbal/written communication (CET 6 or equivalent experience) & data presentation skills, including an ability to effectively communicate with both business and technical. Experience with large scale analytics paradigms (Map Reduce, NoSQL). Knowledge with supervised learning methods (linear and logistic regression, generalized linear models, decision trees, random forests, support vector machines, graphical models, neural networks, anomaly detection etc.). Knowledge with unsupervised learning methods (K-means, hierarchical clustering, association rules, principal components, etc.). Very strong self-learning skills. Ability to pick up and adapt modeling methods from other disciplines or leverage methods from other skilled colleagues in other departments in solving problems. Strong organizational, time management, communication, and engineering skills are necessary. [详情]
我要投递简历