Competitive Data Scientist (Kaggle)
** 薪水：** 每小时60美元至120美元 (USD). ** 角色标题：** 竞争数据科学家 (Kaggle) ** 薪资：** 60美元每小时120美元 ** 地点：** 全球，完全远程 ** 工作类型：** 承包商 （~15个小时/周）** 日程：** 灵活 - 你选择你工作的时间和日期，包括周末如果需要 我们正在寻找具有竞争性数据科学经验的数据科学家，机器学习从业者和应用数学专家。（Kagcle,驱动数据等）你将利用竞争体验，数学推理和编程技能创建不仅需要合理答案的工作任务。每个任务都应该有一个可复制的环境，一个验证的参考解决方案和可靠的检查以确保成功完成。**What Youll Do** 1. 建立可复制环境，准备相关数据和依赖性，并实施参考解决方案。2.制定测试和评估标准，可可靠地区分正确的解决方案与不完整或缺陷的方法。3. 对任务进行编码代理，调查其故障，并根据观察行为来精细化任务。4.确定实现错误，假设缺陷，数据泄漏和其他影响正确性的问题。5. 清楚记录任务要求，解决方案推理和验证步骤。**我们正在寻找什么** 1.在数据科学，ML,量化建模或相关研究竞赛中经验证明。（Kaggle,DrivenData 等）2.强大的解决问题能力和清楚了解你使用的方法。3.足够实践编程能力来实现和验证自己的解决方案。相关竞争平台包括 Kaggle,DriveData,Zindi,AIcrowd,Numerai,DataSource.ai 和 CodaLab/Codabench. **Huge Plus** 1.强大的编码经验，特别是在 Python 上，包括测试，调整和与未熟悉代码一起工作。
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**Pay:** $60-$120 per hour (USD).
**Role Title:** Competitive Data Scientist (Kaggle)
**Pay:** $60–$120/hour
**Location:** Global, fully remote
**Job Type:** Contractor (~15 hours per week)
**Schedule:** Flexible-you choose the hours and days you work, including weekends if desired
We're looking for data scientists, machine learning practitioners, and applied mathematics experts with competitve Data Science experience. (Kaggle, DrivenData, etc.)
You’ll draw on your competition experience, mathematical reasoning, and programming skills to create tasks that require more than a plausible answer. Each task should have a reproducible environment, a verified reference solution, and reliable checks for successful completion.
**What You’ll Do**
1. Build reproducible environments, prepare relevant data and dependencies, and implement reference solutions.
2. Develop tests and evaluation criteria that reliably distinguish correct solutions from incomplete or flawed approaches.
3. Run coding agents against tasks, investigate their failures, and refine the tasks based on observed behavior.
4. Identify implementation errors, flawed assumptions, data leakage, and other issues that affect correctness.
5. Document task requirements, solution reasoning, and verification steps clearly.
**What We’re Looking For**
1. Demonstrated experience in data science, ML, quantitative modeling, or relevant research competitions. (Kaggle, DrivenData, etc.)
2. Strong problem-solving ability and a clear understanding of the methods you use.
3. Practical programming ability sufficient to implement and verify your own solutions.
Relevant competition platforms include Kaggle, DrivenData, Zindi, AIcrowd, Numerai, DataSource.ai, and CodaLab/Codabench.
**Huge Pluses**
1. Strong coding experience, particularly in Python, including testing, debugging, and working with unfamiliar code