> Markdown version of [/jobs/ext/4532984-competitive-data-scientist-kaggle-remote-contract](https://www.wearedevelopers.com/jobs/ext/4532984-competitive-data-scientist-kaggle-remote-contract). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Competitive Data Scientist (Kaggle) (Remote, Contract) - **Company:** micro1 - **Location:** UK (Remote available) - **Salary:** £97,760.0 - £197,600.0 - **Contract:** Permanent contract - **Skills:** Computer Programming, Information Leak Prevention, Software Debugging, Python (Programming Language), Machine Learning, Kaggle - **Published:** November 3, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5950599734 ## About the Role * Demonstrated experience in data science, ML, quantitative modeling, or relevant research competitions. (Kaggle, DrivenData, etc.) * Strong problem-solving ability and a clear understanding of the methods you use. * 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., * Strong coding experience, particularly in Python, including testing, debugging, and working with unfamiliar code ## Description 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., * Build reproducible environments, prepare relevant data and dependencies, and implement reference solutions. * Develop tests and evaluation criteria that reliably distinguish correct solutions from incomplete or flawed approaches. * Run coding agents against tasks, investigate their failures, and refine the tasks based on observed behavior. * Identify implementation errors, flawed assumptions, data leakage, and other issues that affect correctness. * Document task requirements, solution reasoning, and verification steps clearly.