专业要求：
学历要求：不限
工作经验：不限
薪资待遇：4-8 月薪
招聘人数：1
招聘对象： 社会人才
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工作地区： 西安市 学历要求：大专及以上性别要求：不限
工作经验：2-20 年薪资待遇：3000-30000 月薪招聘人数： 2
公司性质：民营企业公司规模：1 - 49人所属行业：橡胶原料
职位描述：
为满足公司拓展业务的实际需要，特向全国范围内招聘西安办事处橡胶行业商务拓展主管若干。具体要求如下：
我要投递简历
1，要求至少有有机硅橡胶HCR(HTV),LSR行业，合成橡胶（FKM,EPDM,SBR,BR.etc.）行业，橡胶助剂（防老剂，促进剂，硫化剂，软化剂等行业配方，工艺，销售，技术支持等之一的从业经验一年以上，有同行业国际大公司工作经历最佳；
2，要求学历背景为高分子材料，精细化工，有机化学，化学工程本科或研究生学历以上，青岛科技大学，华东理工大学，吉林大学，华南理工大学，北京化工大学，上海交通大学，四川大学，海南大学等高校高分子材料专业毕业生优先考虑； [详情]
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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