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# data scientists

- **Company:** HALTER, LLC
- **Location:** United States (Remote available)
- **Contract:** Contract
- **Skills:** Computer Programming, Information Leak Prevention, Software Debugging, Python (Programming Language), Machine Learning, Kaggle
- **Published:** October 15, 2026
- **Apply:** https://www.builtincolorado.com/job/competitive-data-scientist-remote-contract/11661778?handler=ApplyRedirect

## About the Role

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., * ML Engineering

 * Data Science
 * Mathematics
 * Coding, * 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.

Huge Pluses

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

## Description

Remote
Hiring Remotely in United States
Entry level
Remote
Hiring Remotely in United States
Entry level
Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.
The summary above was generated by AI, * 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.