Competitive Data Scientist

Great Value Hiring; United Kingdom; On-site; GBP 62,000 - 124,000; Part time

Job summary

Great Value Hiring is seeking a data scientist to build reproducible environments, prepare data, and implement reference solutions for complex tasks. The role involves developing tests and evaluation criteria to distinguish correct solutions from flawed approaches, and running coding agents to probe and refine tasks based on observed behavior.

Ideal candidates have experience in data science, ML, or quantitative modeling, plus strong problem-solving skills and practical programming ability to

Qualifications

• 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

Responsibilities

• 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

Skills

Data science

Machine Learning

Quantitative modeling

Coding ability

Problem solving

Tools

Python

Job description

Competitive Data Scientist [$60 - $120/hour]

Role Responsibilities

• 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

Good Candidature

• 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.

Nice to Have

• Strong coding experience, particularly in Python, including testing, debugging, and working with unfamiliar code