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部门介绍
No.1
Introduction
 Introduction 
PwC AC Shanghai Analytic Insights Team专注于先进的分析技能，通过企业级数据管理、商业智能、机器学习、云计算大数据等技术的整合应用与客户进行多维度的接洽。
 Introduction 
PwC AC Shanghai Analytic Insights focuses on advanced analytics skills, approaching clients from multi-dimensional perspectives via integrated capabilities in Enterprise Data Management, Business Intelligence, Machine Learning, Big Data and Cloud Computing.
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目前团队有来自世界顶尖高校毕业的数据科学家，精通于预测、规范性及感知分析，AI建模，基于业务的数据科学原型设计和模型实施能力。在金融服务、医疗健康、消费市场及零售、工业产品服务、政府和公共服务分析技能方面，我们拥有平均7年行业经验的咨询顾问，利用价值驱动思维和以客户为导向的方法提供有合作性的，透明的客户服务。还有几十位拥有平均5年以上经验的专业数据工程师，可通过交互式可视化管理企业的整个数据生命周期，展现商业见解。
The current team has data scientists who graduated from the world's top universities. They are proficient in predictive, prescriptive and cognitive analytics & AI Modeling, business-oriented data science prototyping. Also, we have functional consultants with an average of seven-year industrial experience in FS, HIA, CM, IPS, Government & Public Service, utilizing value-driven thinking and client-oriented approaches to provide collaborative and transparent client engagement. We also have dozens of professional data engineers with an average of 5+ years of experience who oversee the data lifecycle of enterprises and demonstrate sharp business insights through interactive visualization.
市场环境
No.2
Business Environment
Business Environment 
人工智能及大数据方向一直是世界上各个国家、企业事业争相投入，并且竞争激烈的新兴产业。 PwC AC Shanghai Analytic Insights部门致力于站在业内前沿，研发行业驱动、数智赋能的定制化及转型服务，并通过论文，白皮书及专利，将学术与生产有机结合起来。
Business Environment 
Artificial intelligence and big data have always been competitive fields for investment from all over the world. PwC AC Shanghai Analytic Insights is committed to standing at the forefront of the business, developing customized and transformation services driven by industry insights and enabled by digital intelligence, and combining academic research and product development through publications, white papers and patents.
主流技术
No.3
Major Technical Focus
01
自然语言处理 
Natural Language Processing
基于BERT及其变种的命名实体识别，实体关系提取。
Named Entity Recognition and Entity Relation Extraction based on BERT model and its variants.
02
自然语言生成 
Natural Language Generation
根据知识图谱，利用GPT模型进行自然语言生成。
Natural language generation using GPT model and knowledge graph.
03
计算机视觉图像处理
Computer Vision Image Processing
应用transformers模型进行图片中物体检测，对抗神经网络进行高清晰图片生成；多模态模型进行图片解释及问答，基于自然语言的图片搜索等。
Objective detection using transformers, high-resolution images generation using generative adversarial neural networks; 
Multimodality model for image captioning, multi-modal model for picture interpretation and Q&A, and natural language based image search, etc.
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Analytic Insights Team
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01
ETL-Informatica
岗位职责:
1. 使用结构化软件开发方法设计、开发和实施提取、转换和加载 (ETL) 流程、程序和脚本。
2. 使用标准建模技术开发数据/元数据模型和技术规范。
3. 咨询分析师、数据提供者和业务运营人员，了解和阐述数据发布需求并提出技术解决方案。
4. 评估数据提供者的数据产品需求，并将需求转化为技术规范。
5. 确定技术标准； 评估替代协议、工具和标准。
6. 进行根本原因分析，解决生产问题。
7. 为知识共享和交接创建已实施 ETL 流程的文档。
8. 制定设计估算。
岗位要求:
1. 3年以上 informatica 工具相关开发工作、ETL 方法和技术的经验。
2. 对数据仓库概念有深刻理解。
3. 精通SQL编程。有 DB2 或 Oracle 数据库实践经验者优先。
4. 良好的沟通能力，能用英文或日文进行口头和书面表达。
Responsibilities: 
1. Design, develop and implement Extract, Transformation and Load (ETL) processes, programs and scripts using structured software development methods.

2. Develop data/metadata models and technical specifications using standard modeling techniques.

3. Consult with analysts, data providers and business operations staff to understand and elaborate data dissemination requirements and propose technical solutions.

4. Evaluate data product requirements from data providers and translate the requirements into technical specifications. 

5. Define technical standards; evaluate alternative protocols, tools and standards. 

6. Conduct root cause analysis and resolve production problems. 

7. Create documentation of implemented ETL processes for knowledge sharing and hand-off.

8. Develop design estimates.  
Requirements: 
1. Three or more years of experience with informatica tool-related development work, ETL approach and technologies. 

2. Strong understanding of data warehouse concepts. 

3. Proficient in SQL programming. Hands-on experience in DB2 or Oracle Database is preferred. 

4. Good communication skills, verbal and written in English or Japanese.
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02
Business Intelligence
岗位职责:
1.咨询分析师，数据提供商和业务运营人员合作以了解和阐述数据可视化或仪表板报告要求，并提出技术解决方案。
2.评估数据提供方的数据产品要求，并将要求转化为技术规范。
3.定义技术标准; 评估替代协议，工具和标准。
4.进行根本原因分析并解决产品问题。
5.创建已实施报告流程的记录以共享和转换知识。
6.制定设计估算。
7.使用结构化软件开发方法开发和实现提取，转换和加载（ETL）过程，程序和脚本。
8.使用报告层中的标准建模技术开发数据/元数据模型和技术规范。
岗位要求:
1.3年或以上与BI报告工具相关的开发工作经验，例如Tableau / Power BI / Qlikview / Spotfire。 （Tableau经验优先）。
2.对BI / 数据仓库概念有深刻理解。
3.精通SQL编程。Oracle，SQL Server，Hive，Impala或DB2数据库的实践经验优先。
4.具有在AWS，Azure等云平台上工作的经验。
5.Spark / Scala / Cloudera等大数据开发经验优先。
6.掌握Python，SAS，R，Alteryx等语言。
7.良好的沟通技巧，良好的口语和书面英语水平。
8.积极，热情和擅长自我激励。
Responsibilities：
1.Consult with analysts, data providers and business operations staff to understand and elaborate data visualization or dashboard reporting requirements and propose technical solutions. 
2.Evaluate data product requirements from data providers and translate the requirements into technical specifications. 
3.Define technical standards; evaluate alternative protocols, tools and standards. 
4.Conduct root cause analysis and resolve production problems. 
5.Create documentation of implemented reporting processes for knowledge sharing and hand-off.
6.Develop design estimates.  
7.Develop and implement Extract, Transformation and Load (ETL) processes, programs and scripts using structured software development methods.
8.Develop data/metadata models and technical specifications using standard modeling techniques in the reporting layer.
Requirements: 
1. Three years or more of experience with BI reporting tool-related development work, such as Tableau/ Power BI/Qlikview/ Spotfire (Tableau experience would be more preferred). 
2.Strong understanding in BI/data warehouse concepts. 
3.Proficient in SQL programming. Hands-on experience in Oracle, SQL Server, Hive, Impala or DB2 Database is preferred.
4.Have experience working on cloud platforms such as AWS, Azure etc.
5.Big Data experiences such as Spark/Scala/Cloudera is a plus.
6.Skills of Python, SAS, R, Alteryx would be a plus.
7.Good communication skills in verbal and written English.
8.Positive, enthusiastic and self-motivated.
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03
Big Data

岗位要求:
1.计算机科学、数学、信息系统或工程专业的学士或以上学位。
2.至少1年的大数据、关系型数据库、NoSQL和数据库解决方案开发数据摄取、数据处理和分析方面的经验。
3.精通Databricks（Spark）和Azure云技术。
4.拥有至少1年的大数据技术和框架经验，包括但不限于HDFS、Hadoop、MapReduce、Spark、Hive、HBase、Cassandra和ElasticSearch、Kafka、Flume、Flink、Airflow、Databricks等。
5.掌握至少一种编程语言，如Python, Scala, Java。
6.在数据库开发和SQL脚本方面有坚实的技术背景。
7.具有大型云计算基础设施中开发大数据解决方案的经验，如亚马逊网络服务，微软Azure或谷歌云。
8.有企业内部或云端从头建立Hadoop集群环境经验者优先。
9.优秀的英语口语和写作能力。
10.有Scrum或同等敏捷开发流程的工作经验。
Responsibilities：
1.Bachelor’s or above degree in computer science,mathematics, information systems or engineering or equivalent technology domain experience.
2.At least 1 years of experience in developing data ingestion, data processing and analytical pipelines for big data, relational databases, NoSQL and data warehouse solutions.
3.Proficient in Databricks (Spark) and Azure cloud technology.
4.Minimum 1 year experience with big data technologies and frameworks including but not limited to HDFS, Hadoop, MapReduce, Spark, Hive, HBase, Cassandra and ElasticSearch, Kafka, Flume, Flink, Airflow,Databricks etc.
5.Knowledge of at least one programming language such as Python, Scala, Java.
6.Solid technical background in database development and SQL Scripts.
7.Experience with big data solutions developed in large cloud computing infrastructure such as Amazon Web Service, Microsoft Azure or Google Cloud.
8.Experience of building Hadoop clusters environment on-premises or on cloud from scratch would be a plus.
9.Excellent English oral and written English.
10.Experience working with Scrum or equivalent agile development process.
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04
Data Engineer (Azure/AWS)
岗位职责：
1.负责开发工作流程，将数据从各种类型的源系统设入到S3、RDS和DynamoDB。
2.编写自动化流程，从源系统API、批量数据馈送、内部数据库等摄取数据。
3.设计无服务器、事件驱动架构，在适用的情况下摄取数据。
岗位要求：
1.计算机科学或相关学科本科及以上学位。
2.至少3年Azure咨询或客户服务交付经验。
3.精通SQL。熟悉Python/R和SQL所需技能。
4.熟悉AWS SFTP, 数据库移服务, DataSync, 模式转换工具。有AWS数据湖经验。
5.对VPC，EC2，IAM，KMS和云计算形成有深入的产品知识。
6.有Redshift和Quicksight的经验。
7.有Lambda、Step Functions、Serverless/SAM框架、DynamoDB经验。
8.有Devops流程和构建CI/CD管道经验.
9.熟悉S3、亚马逊RDS for PostgreSQL、Redshift、Quicksight。
10.Lambda, Step Functions, Serverless FW, DynamoDB.
以下技能者优先：
1.DP-200 实施Azure数据解决方案/ DP-201 设计Azure数据解决方案 。
2.在Azure平台上的DevOps。
3.在Azure上开发和部署ETL解决方案的经验。
4.物联网、事件驱动、微服务、云中的容器/Kubernetes。
5.熟悉业界现有的元数据管理技术栈。 数据治理、数据质量、MDM、血统、数据目录等。
6.熟悉行业内用于数据管理、数据摄取、捕获、处理和整理的技术栈。(Kafka, StreamSets, Attunity, GoldenGate, Map Reduce, Hadoop, Hive, Hbase, Cassandra, Spark, Flume, Hive, Impala, etc.)
7.有多云经验者优先 。( Azure, AWS, Google.)
Responsibilities：
1.Responsible for developing workflows to ingest data from various types of source systems to S3, RDS and DynamoDB.
2. Write automated processes to ingest data from source system APIs, bulk data feeds, internal databases, etc.
3. Design serverless, event-driven architectures to ingest data where applicable.
Requirements: 
1. Bachelor's degree or higher in Computer Science or related discipline.
2. At least 3 years of experience in Azure consulting or customer service delivery.
3. Proficiency in SQL. familiarity with Python/R and SQL required skills.
4. Familiarity with AWS SFTP, Database Migration Service, DataSync, schema transformation tools. Experience with AWS data lakes.
5. In-depth product knowledge of VPC, EC2, IAM, KMS and cloud formation.
6. Experience with Redshift and Quicksight.
7. Experience with Lambda, Step Functions, Serverless/SAM frameworks, DynamoDB.
8. Experience with Devops process and building CI/CD pipelines.
9. Familiar with S3, Amazon RDS for PostgreSQL, Redshift, Quicksight.
10.Lambda, Step Functions, Serverless FW, DynamoDB.
Nice-to-have Skills/Qualifications:
1.DP-200 Implementing Azure data solutions/ DP-201 Designing Azure data solutions .
2.DevOps on an Azure platform.
3.Experience developing and deploying ETL solutions on Azure
4.IoT, event-driven, microservices, Containers/Kubernetes in the cloud
5.Familiarity with the technology stack available in the industry for metadata management:  Data Governance, Data Quality, MDM, Lineage, Data Catalog etc.
6.Familiarity with the Technology stack available in the industry for data management, data ingestion, capture, processing and curation:  Kafka, StreamSets, Attunity, GoldenGate, Map Reduce, Hadoop, Hive, Hbase, Cassandra, Spark, Flume, Hive, Impala, etc.
7.Multi-cloud experience a plus - Azure, AWS, Google.
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05
Data Scientist
岗位职责：
1.负责创新应用的模型/策略/算法的架构设计，包括但不限于预测分析、自然语言处理等。
2.确定收集新数据的独特机会。
3.设计新的流程并建立大型的、复杂的数据集。
4.对数据的新用途及其与数据设计的互动进行策略设计。
5.寻找新的数据源，分析统计数据并实施质量程序。
6.对新的和不同的数据源进行数据研究。
7.为现有数据源寻找新的用途。
8.进行统计建模和实验设计。
9.发现数据所讲述的 "故事"，并将其介绍给其他科学家和业务经理。
10.测试和验证预测模型。
11.构建网络原型并进行数据可视化。
12.在云上和云下进行可扩展的数据研究。
13.实施自动化流程，有效地生产规模模型。
14.设计、修改和建立新的数据流程。
15.生成算法并创建计算机模型。
16.与数据库工程师和其他科学家合作。
17.实施新的或增强的软件，旨在更有效地访问和处理数据。
18.对数据管理团队进行新的或更新的程序培训。
19.保持最新，并教育团队在分析应用的最前沿。
岗位要求：
1.数学、计算机科学或类似研究领域的硕士学位或博士学位，至少有3-5年的相关经验。
2.有数据挖掘或自然语言处理方面的工作经验。
3.具备统计学、编程和预测模型的工作知识。
4.优秀的代码编写能力。
5.具有较强的批判性思维能力，并能将其与公司正在生产的产品或服务联系起来。
6.精通Python、R和SQL（结构化查询语言）的编程。
7.Hadoop、NoSQL、Java是强有力的补充。
8.在可扩展/大数据语言上编程，如Scala/Spark是一个加分项。
9.对基本和高级预测模型有很深的了解。
10.拥有创新能力和商业知识的结合。
11.拥有跨越一系列学科的数据挖掘知识。
12.卓越的技术编码能力。
13.较强的探索性分析能力。
14.优秀的口头和书面沟通能力，以及在数据科学和商业管理之间搭建桥梁的能力。
15.具有创造性地思考和研究的能力。
16.卓越的组织能力，注重细节。
17.对统计学、机器学习、算法和高级数学有一定的掌握。
18.有较强的书面和口头英语交流能力。
19.能够在短时间内获得和应用广阔知识领域的高水平技能，并有强烈的个人责任感。
Responsibilities：
1.Responsible for the architecture design of models / strategies / algorithms for the innovative applications, including but not limited to predictive analysis, natural language processing etc.
2.Identify unique opportunities to collect new data. 
3.Design new processes and build up large, complex data sets.
4.Strategize new uses for data and its interaction with data design.
5.Locate new data sources, analyze statistics and implement quality procedures.
6.Perform data studies of new and diverse data sources.
7.Find new uses for existing data sources.
8.Conduct statistical modeling and experiment design.
9.Discover “stories” told by the data and present them to other scientists and business managers.
10.Test and validate predictive models.
11.Build web prototypes and perform data visualization.
12.Conduct scalable data research on and off the cloud.
13.Implement automated processes for efficiently producing scale models.
14.Design, modify and build new data processes.
15.Generate algorithms and create computer models.
16.Collaborate with database engineers and other scientists.
17.Implement new or enhanced software designed to access and handle data more efficiently.
18.Train the data management team on new or updated procedures.
19.Stay up to date and educate the team on the cutting edge of analytic applications.
Requirements: 
1. Master's degree or PhD in mathematics, computer science or similar research field.
2. Working experience in data mining or natural language processing.
3. Working knowledge of statistics, programming, and predictive modeling.
4. Excellent coding skills.
5. Strong critical thinking skills and the ability to relate them to the products or services being produced by the company.
6. Proficient in programming in Python, R and SQL (Structured Query Language).
7. Hadoop, NoSQL, Java are strong complements.
8. Programming on scalable/big data languages such as Scala/Spark is a plus.
9. Strong understanding of basic and advanced predictive models.
10. Possess a combination of innovation skills and business knowledge.
11. Possess knowledge of data mining across a range of disciplines.
12. Excellent technical coding skills.
13. Strong exploratory analytical skills.
14. Excellent verbal and written communication skills and the ability to bridge the gap between data science and business management.
15. Ability to think and research creatively.
16. Excellent organizational skills and attention to detail.
17. Demonstrated mastery of statistics, machine learning, algorithms, and advanced mathematics.
18. Strong communication skills in English, both on paper and verbally.
19. Ability to acquire and apply high-level skills in a broad area of knowledge in a short period of time, and a strong sense of personal responsibility.
岗位投递
Analytic Insights Team
[email protected]
[email protected]
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