数据科学家（欧洲，亚洲）
Data Scientist (Europe, Asia)
职位描述
作为Sporty的数据科学家，你将构建并实现数据驱动的机器学习系统，提升个性化、搜索和推荐体验，并明确关注可衡量的业务影响。该职位结合高级分析、基于Python的建模和云工程，以解决快节奏产品环境中的实际问题。
我们的技术栈
- SQL
- Python
- AWS/Azure/GCP
你将负责的工作
- 利用先进的算法和数据挖掘技术构建个性化的推荐系统，优化产品和服务推荐，提升用户体验并提高业务转化率。
- 在多个领域（包括搜索排名、推荐和内容智能）开发并实现机器学习/AI系统，直接影响用户增长和业务成果。
你将带来的能力
- 有将统计和机器学习模型应用于实际业务问题的经验，并对收入或关键业务成果产生可衡量的影响
- 精通Python及Python的量化分析生态系统
- 扎实的概率论、统计学、机器学习和线性代数基础
- 2年以上数据科学家、量化研究员、量化分析师或其他相关岗位经验
- 熟悉SQL并有关系型数据库使用经验
- 熟练使用AI工具和代理作为日常开发工作流的一部分；该职位经常利用AI辅助开发以加快交付速度
- 应用数学、计算机科学、金融工程、科技或工程专业学位
- 有项目和利益相关者管理经验者优先
如果你有以下经历会更佳
- Apache Spark
- 有在云平台（AWS、GCP、Microsoft Azure）工作的经验
- 有游戏行业相关知识或经验
- 扎实的计算机科学背景
你的收益
- Sporty是一家以远程办公为先的公司，致力于可持续发展
- 竞争性的薪资 + 每季度根据个人表现发放的奖金
- 28天带薪年假
- 我们的核心工作时间是您时区的上午10点至下午3点，此时间段外有灵活性
- 推荐奖金和即时奖金
- 高端设备
- 年度公司团建活动，提供良好的内部交流机会
面试流程
- 与我们的招聘团队进行远程视频初筛
- 在线家庭作业
- 与团队成员进行远程视频面试（3次，每次45分钟）
查看英文原文
About the role
As a Data Scientist at Sporty, you will build and productionize data-driven ML systems that enhance personalization, search, and recommendation experiences, with a clear focus on measurable business impact. The role combines advanced analytics, Python-based modelling, and cloud engineering to solve real-world problems at scale in a fast-paced product environment.
Our Stack
- SQL
- Python
- AWS/Azure/GCP
What you'll be doing
- Utilize advanced algorithms and data mining techniques to build personalized recommendation systems, optimizing product and service recommendations to enhance user experience and improve business conversion rates.
- Develop and productionize ML/AI systems across multiple domains — including search ranking, recommendation, and content intelligence — that directly impact user growth and business outcomes.
What you'll bring
- Hands-on experience applying statistical and ML models to real business problems, with measurable impact on revenue or key business outcomes
- Advanced understanding of Python and the quantitative analysis ecosystem in Python
- Strong foundation in probability theory, statistics, machine learning, and linear algebra
- 2+ years of experience as a data scientist, quantitative researcher, quantitative analyst, or another relevant role
- Knowledge of SQL and experience with relational databases
- Proficient with AI tools and agents as part of daily development workflow; this role regularly leverages AI-assisted development to accelerate delivery
- Degree in Applied Mathematics, Computer Science, Financial Engineering, Technology, or Engineering
- Project and stakeholder management experience is preferred
Even better if you have
- Apache Spark
- Experience working in cloud platforms (AWS, GCP, Microsoft Azure)
- Relevant knowledge or experience in the gaming industry
- Solid computer science background
What’s in it for you
- Sporty is a remote first company in pursuit of sustainability
- A competitive salary + individual performance based bonuses every quarter
- 28 days paid annual leave
- Our core working hours are 10am-3pm in your local time zone with flexibility outside of this
- Referral bonuses & flash bonuses
- Top of the line equipment
- Annual company retreats to provide great internal networking opportunities
Interview Process
- Remote video screening with our Talent Acquisition Team
- Online home assignment
- Remote video interview with Team Members (3x45 Mins)
If you're interested, we encourage you to apply! Every application is reviewed by a member of our team (AI is not used in our recruitment process), and we aim to respond within 48 hours.
*We are committed to maintaining a fair, secure and authentic recruitment process. Applications submitted using false, misleading or fraudulent information, including impersonated or fake profiles, will not be considered. Where reasonably necessary to protect the integrity and security of our recruitment process, we may verify a candidate’s identity and/or the accuracy of information provided during the recruitment process. Any such checks will be carried out in a proportionate and appropriate manner and in accordance with applicable data protection and employment laws.