data scientist 的定义
A data scientist (or QFP) with deep experience in one or more areas of data science, but pervasive exposure to all data science techniques / models. Ready to explore new data sets from clients, enable clients in the usage of IBM-provided data or models, or create new algorithms. Broad exposure to IBM tools and general data science models is critical. SPSS or similar tool usage at an expert level. Data science professionals provide client-facing services for our next generation cognitive predictive analytics solution, providing insights Powered by Watson Analytics. They work with IBM's clients and industry experts to correctly frame problems for the customer, analyze their data, apply machine learning technologies, and develop predictive insights helping analyze and address business problems, predict the future, or take action. Typically data scientists should have at least a Masters degree in data science, statistics, or related field and have expertise in machine learning, statistics, and data architectures. Experience with data science environments R, Python, SAS, or SPSS is very important. They have strong knowledge of Machine Learning techniques for regression, classification, clustering, and forecasting. Professional experience in addition to academic is important. Good communication and teaming skills are critical. We look for professionals with: Extensive hands on experience working with very large data sets, including statistical analysis, data visualization, data mining, and data cleansing/transformation. Strong ability to communicate analytical results in a way that is meaningful for business stakeholders and provides actionable insights. Has successfully demonstrated in prior experience the ability to discover new opportunities where analytical techniques can be leveraged for solving business problems across the company. Skills and Experience Minimum 3-5 years relevant experience with an organization known for its cutting edge/ best-in-class applicability of advanced analytics and predictive modeling techniques. Master's Degree / PhD in a quantitative field (e.g., Computer Science, Economics, Engineering, Mathematics, Finance, Statistics, Operations Research, Physics). Extensive knowledge of and experience in applying data mining and machine learning techniques in a professional context. High level of comfort with various data types and structures: structured versus unstructured data, or static versus stream data. Extensive prior experience in integrating data, profiling, validating and cleansing data. High level of proficiency in statistical tools like SAS, R, SPSS. Well versed/ good understanding of programming languages like Java/C/C++ in order to generate derived data. Experience with Hadoop, MapReduce, PIG and relational databases and SQL is preferred. Excellent troubleshooting skills. Excellent negotiation skills. Excellent written and verbal communication skills. Fluency in English. Experience of working within an Agile environment.
数据科学家(或QFP)与深度体验在一个或多个数据的科学领域,但普遍暴露在所有数据科学技术/模型。准备探索新的数据集来自客户,使客户在使用ibm提供的数据或模型,或创建新的算法。广泛接触IBM工具和通用数据科学模型是至关重要的。
在专家层面SPSS或类似的工具使用。数据科学专业人士为我们的下一代提供面向客户的服务认知预测分析的解决方案,提供见解由沃森分析。他们的工作与IBM的客户和行业专家为客户正确的框架问题,分析他们的数据,应用机器学习技术,和发展预测的见解帮助分析和解决业务问题,预测未来,或采取行动。通常数据科学家应该至少硕士学位数据科学、统计、机器学习中或相关专业,有专业知识,统计和数据架构。经验与数据科学环境R,Python,SAS和SPSS是非常重要的。他们有强烈的机器学习技术知识回归,分类、聚类、预测。专业经验除了学术是很重要的。良好的沟通和合作技能是至关重要的。
我们寻找专业人士:广泛的手在工作经验上非常大的数据集,包括统计分析、数据可视化、数据挖掘和数据清理/转换。强大的沟通能力分析结果的方式是有意义的业务涉众,并提供可行的见解。已经成功演示了在经验能够发现新的机会,可以利用分析技术为解决整个公司业务问题。技能和经验3 - 5年以上相关经验与一个组织以其前沿/一流先进的分析和预测建模技术的适用性。硕士/博士学位数量字段(如。、计算机科学、经济学、工程学、数学、金融、统计、运筹学、物理)。广泛的知识和经验在数据挖掘和机器学习技术的应用在专业背景。高水平的安慰与不同的数据类型和结构:结构化和非结构化数据,或者静态和流数据。广泛的集成经验数据、分析、验证和清理数据。高水平的能力在SAS等统计工具,R,SPSS。工/好理解的编程语言如Java / C / c++为了生成派生数据。体验使用Hadoop MapReduce,pig 和关系数据库和SQL是首选。优秀的故障诊断能力。优秀的谈判技巧。优秀的书面和口头沟通技巧。流利的英语。在敏捷的环境中工作的经验。