远程工作雷达

高级数据科学家

Senior Data Scientist

AI限定地区(需当地身份)
公司Ex Parte
薪资未公开
工作地点United States
地域资格限定地区(需当地身份)
时区要求日间重叠约 9 小时,基本正常作息
用工类型Full Time
发布时间今天
数据来源Himalayas
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注意地域限制:该职位明确限定在 United States 招聘。如果你是位于中国大陆的求职者,通常需要当地工作身份才能投递,或需与雇主确认是否接受独立合同(Contractor)形式合作。
  • 推动技术路线图,将风险监控扩展到已识别的威胁面
  • 开发/实验/部署最先进的预测模型
  • 使用优秀的数据科学实践,迭代产出高性能模型
  • 通过可靠且实用的风险监控交付实现即时影响
  • 与工程同事合作,通过数据可视化传达发现
  • 测量、调整并优化现有算法,逐步提升性能
  • 在以新颖方式提取、转换和组合数据后,分析新旧数据
  • 向工程和运维团队传达需求,确保健康的反馈循环
  • 吸引并引入新人才,同时保持并提升现有文化
  • 与其他团队建立牢固的合作关系,跨企业协作
  • 展现对团队工作的主人翁意识和个人责任感

基本要求:

  • 5年以上应用数据科学经验,包括3年以上专注于企业特定问题解决的高级分析经验
  • 2年以上管理、指导或其他相关团队或人员领导经验
  • 具有机器学习(监督式、半监督式或无监督式学习)经验
  • 强大的沟通能力、交付管理和领导能力

优先考虑:

  • 统计学、数据科学、人工智能或同等替代教育或经验的学士学位、硕士或博士学位
  • 具有SQL的实际经验,以及Python或R或Scala,以及现代数据科学工具/包的实际经验,例如PyTorch、Transformers、TensorFlow、scikit-learn
  • 具有Databricks和/或Azure ML的实际经验
  • 具有至少一种脚本语言(如Python或SQL)的强大编码能力
  • 理解合规性、安全性和风险领域及其相关模式和数据元素
  • 理解产品和服务激活、使用和交易模型及数据
  • 理解统计分析和机器学习工具及实践
  • 理解以云为中心的数据处理和可视化方法,包括SQL和NoSQL数据库,并具备Azure SQL、Azure Cosmos DB、Data Factory、Synapse、Azure Data Lake等的经验
  • 熟悉敏捷软件交付,包括应用生命周期管理(SAFe、Azure DevOps/VSTS、Git)
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  • Drive technical roadmap to extend risk monitoring across identified threat surfaces.
  • Develop/experiment/ship state-of-the-art prediction models
  • Use excellent data science practices to iteratively produce high performing models
  • ​​​​​​Create immediate impact through sound and practical deliveries of risk monitors
  • Work with engineering colleagues to convey findings through data visualizations
  • Measure, tune and refine existing algorithms to incrementally improve performance
  • Analyze new and existing data after extracting. transforming and combining it in novel ways
  • Convey needs to engineering and operations teams to ensure healthy feedback loops
  • Attract and onboard new talent while preserving and enhancing existing culture
  • Build strong partnerships and collaborate with other teams across the enterprise
  • Demonstrate sense ownership and personal accountability for your team's work

Basic Qualifications:

  • 5+ years applied data science experience, including 3 years of advanced analytics experience focused on enterprise-specific problem solving
  • 2+ years management, mentoring, or other closely related team or people leadership experience
  • Experience in machine learning (supervised, semi-supervised or unsupervised learning)
  • Strong communication, delivery management, and leadership skills

Preferred Qualifications

  • A bachelor’s degree, MSc or Ph.D. in Statistics, Data Science, Artificial Intelligence, or equivalent alternative education or experience
  • Applied experience with SQL, also Python or R or Scala, and modern data science tools/packages e.g. PyTorch, Transformers, TensorFlow, scikit-learn
  • Applied experience with Databricks and/or Azure ML
  • Strong coding abilities in one or more scripting languages like Python or SQL
  • Understanding of compliance, security, and risk domains along with associated patterns and data elements
  • Understanding of product and services activation, use, and transaction models and data
  • Understanding of statistical analysis and machine learning tools and practices
  • Understanding of Cloud-centric data processing and visualization approaches including SQL and NoSQL databases with exposure to Azure SQL, Azure Cosmos DB, Data Factory, Synapse, Azure Data Lake, etc
  • Familiarity with Agile software delivery including application lifecycle mgmt (SAFe, Azure DevOps/VSTS, Git)

All your information will be kept confidential according to EEO guidelines.
Ex Parte provides our customers with the data and insight to make smart and informed decisions on the most important legal issues facing their organizations.
We are is looking for talented, enthusiastic senior data engineers who share our passion for big data, AI, and machine learning and are excited by seemingly-impossible challenges. As an early employee, you must be amazingly entrepreneurial and thrive in a fast-paced environment where the solutions aren’t predefined.
Every year, corporations spend more than $250B on litigation in the United States alone. And yet, critical decisions such as whether to litigate or settle, or where to file suit or which attorney to hire, are all made the same way they were 100 years ago.
We are applying artificial intelligence, machine learning, and natural language processing to provide our customers with the insight they need to make highly informed decisions and gain a winning advantage. Think of it like Moneyball, but for a market more than 20x the size of Major League Baseball.
Originally posted on Himalayas

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