足球数据科学家 - 欧洲
Soccer Data Scientist - Europe
公司简介
Swish Analytics 是一家体育分析、投注和幻想体育初创公司，正在打造下一代预测体育分析数据产品。我们认为，赔率制定是一项根植于工程学、数学和体育投注专业知识的挑战，而不是直觉。我们寻找具有真实热情、追求准确和预测性实时数据，并能在快节奏、富有创造力且不断演进的环境中执行任务，同时不牺牲技术卓越性的团队成员。我们的挑战是独特的，因此我们希望你能在未知领域中感到舒适，并热衷于构建支持多种行业和消费者/企业客户的系统。
职位描述
Swish Analytics 正在招聘足球数据科学家加入我们不断壮大的团队！数据科学是我们业务的核心，因此这个团队对开发 Swish 数据产品的核心组件有真正的所有权和影响力。我们正在招聘一名数据科学家来支持我们的体育数据模型。
职责：
- 构思、开发和改进驱动 Swish 核心算法的机器学习和统计模型，用于生成最先进的体育博彩产品。
- 使用特定领域的足球知识开发上下文相关的特征集。
- 参与模型开发的所有阶段，从创建概念验证和测试，到与数据工程和产品团队合作部署新模型。
- 通过从严格的离线和在线实验中获得的见解，持续提升模型性能。
- 分析结果和输出，评估模型性能并识别模型弱点，以指导开发工作。
- 遵循软件工程最佳实践，并为共享代码仓库做出贡献。
- 记录建模工作并向利益相关者和其他技术和非技术合作伙伴进行汇报。
要求：
- 数据分析、数据科学、计算机科学或相关技术领域的硕士学位
- 在足球或体育博彩领域展示出在生产规模上开发模型的经验
- 在概率论、机器学习、推断统计学、贝叶斯统计学、马尔可夫链蒙特卡洛方法方面的专业知识
- 至少 3 年以上在体育或体育博彩领域展示出开发和交付有效机器学习和/或统计模型以满足业务需求的经验
- 熟悉关系型 SQL 和 Python
- 有使用源代码控制工具的经验
查看英文原文
Company Description
Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.
Job Description
Swish Analytics is hiring Soccer Data Scientists to join our ever-growing team! Data Science is at the core of our business, so this team has true ownership and impact over developing core components of Swish's data products. We're hiring a Data Scientist to support our Sports Data Models.
Duties:
- Ideate, develop and improve machine learning and statistical models that drive Swish’s core algorithms for producing state-of-the-art sports betting products.
- Develop contextualized feature sets using specific domain knowledge in soccer.
- Contribute to all stages of model development, from creating proof-of-concepts and beta testing, to partnering with data engineering and product teams to deploy new models.
- Strive to constantly improve model performance using insights from rigorous offline and online experimentation.
- Analyze results and outputs to assess model performance and identify model weaknesses for directing development efforts.
- Adhere to software engineering best practices and contribute to shared code repositories.
- Document modeling work and present to stakeholders and other technical and non-technical partners.
Requirements:
- Masters degree in Data Analytics, Data Science, Computer Science or related technical subject area
- Demonstrated experience developing models at production scale for soccer or sports betting
- Expertise in Probability Theory, Machine Learning, Inferential Statistics, Bayesian Statistics, Markov Chain Monte Carlo methods
- Minimum of 3+ years of demonstrated experience developing and delivering effective machine learning and/or statistical models to serve business needs in sports or sports betting
- Experience with relational SQL & Python
- Experience with source control tools such as GitHub and related CI/CD processes
- Experience working in AWS environments etc
- Proven track record of strong leadership skills. Has shown ability to partner with teams in solving complex problems by taking a broad perspective to identify innovative solutions
- Excellent communication skills to both technical and non-technical audiences
Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.
Originally posted on Himalayas