Remote Data Scientist: ML Modeling & Evaluation

Crossing Hurdles; Indonesia; Remote; IDR 1,481,025,000 - 2,962,050,000; Part time

Job summary

Crossing Hurdles is seeking a Competitive Data Scientist for a fully remote contract engagement of 10-40 hours per week. The role involves building reproducible environments, preparing relevant data and dependencies, and implementing reference solutions to complex tasks.

You will develop tests and evaluation criteria to reliably distinguish correct solutions from incomplete or flawed approaches, and run coding agents against tasks to investigate failures and refine the tasks based on observed

Qualifications

• Demonstrated experience in data science, ML, quantitative modeling, or relevant research competitions.
• 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.

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.

Job description

Crossing Hurdles is seeking a Competitive Data Scientist for a fully remote contract engagement of 10-40 hours per week. The role involves building reproducible environments, preparing relevant data and dependencies, and implementing reference solutions to complex tasks.

You will develop tests and evaluation criteria to reliably distinguish correct solutions from incomplete or flawed approaches, and run coding agents against tasks to investigate failures and refine the tasks based on observed