Competitive Data Scientist

EVB; United States; Remote; USD 83,000 - 124,000; Part time

Benefits offered by this job

Competitive package

Job summary

EVB is seeking data scientists, ML practitioners, and applied mathematicians for a contractor role (~15 hours per week) with a flexible schedule. You will create reproducible tasks, build verifiable environments, and craft evaluation criteria to distinguish correct solutions in a competitive data science context.

Applicants should have experience with data science, ML, and competitive platforms (Kaggle, DrivenData), plus strong programming skills in Python for implementing and testing solutions.

Qualifications

• Demonstrated experience in data science, ML, quantitative modeling, or relevant research competitions (Kaggle, DrivenData).
• Strong problem-solving ability and a clear understanding of the methods you use.
• Practical programming ability 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

Kaggle

DrivenData

Python

Tools

Python

Jupyter

Job description

• 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

• 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.

What We're Looking For

• 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

Additional Information

A very attractive and competitive package is offered.