Data Engineer / Data Scientist (Python) – Remote Contract

A company is hiring for a SWE Bench – Data Engineer / Data Scientist role, offering a remote hourly contract for experienced professionals who can work on benchmarking, evaluation workflows, and data-intensive AI tasks.

This is a strong opportunity for candidates with a background in data engineering, data science, or data-focused software engineering who want to contribute to advanced AI evaluation systems through structured experimentation, data pipeline development, and reproducible benchmarking workflows.

If you enjoy working with Python, building clean data pipelines, processing complex datasets, and collaborating on technically challenging AI-related tasks, this role could be an excellent fit.

Job Overview

• Position: SWE Bench – Data Engineer / Data Scientist (Python)
• Work Type: Hourly Contract
• Location: Remote
• Commitment: 20–40 hours per week
• Application Process:
  • Upload resume
  • Technical interview

About the Role

This role focuses on SWE Bench-style evaluation tasks, meaning you’ll be contributing to systems designed to test, benchmark, and evaluate AI performance in realistic software and data environments.

As a contractor in this role, you’ll work across both structured and unstructured datasets to help create, maintain, and validate workflows used in benchmarking and experimentation.

This is not a standard reporting or dashboard role. It is much more aligned with:

• Data pipeline engineering
• Benchmark design
• Data science experimentation
• Validation and reproducibility
• Code quality and technical review
• AI systems evaluation

This makes it especially attractive for technically strong professionals who enjoy working at the intersection of data systems, machine learning, and software engineering.

Core Responsibilities

In this role, you will support SWE Bench-style evaluation tasks by building and validating robust data workflows.

Key Responsibilities Include:

1) Build and Validate Data Pipelines

• Design, build, and validate data pipelines
• Support:
  • Benchmarking systems
  • Evaluation workflows
  • Experimental data processing pipelines

2) Work with Structured & Unstructured Data

• Handle both:
  • Structured datasets
  • Unstructured datasets
• Prepare data for:
  • Analysis
  • Feature engineering
  • Validation
  • Benchmarking use cases

3) Perform Data Processing & Analysis

• Execute and improve workflows for:
  • Data transformation
  • Data cleaning
  • Data validation
  • Feature preparation
  • Analytical processing
  • Data science experimentation

4) Write and Modify Python Code

• Write, run, and modify Python code for:
  • Dataset processing
  • Local experimentation
  • Evaluation tasks
  • Reproducibility checks
  • Workflow automation

5) Validate Outputs for Quality

• Review data transformations and outputs to ensure:
  • Correctness
  • Consistency
  • Reproducibility

6) Create Reusable Workflows

• Build workflows that are:
  • Clean
  • Reusable
  • Well-documented
  • Practical for real-world benchmarking environments

7) Participate in Code Reviews

• Join code reviews to help maintain:
  • High engineering standards
  • Readability
  • Maintainability
  • Documentation quality

8) Collaborate on AI Evaluation Tasks

• Work closely with:
  • Researchers
  • Engineers
• Help design challenging data engineering and data science tasks for AI systems to solve or be evaluated against

Requirements

This role is clearly aimed at experienced professionals with strong technical depth.

Professional Background

You should have strong experience in one or more of the following roles:

• Data Engineer
• Data Scientist
• Data-focused Software Engineer

The description does not state an exact number of years, but it strongly suggests that this is not an entry-level position.

Technical Skills Required

Python Proficiency

You must have strong proficiency in Python, particularly for:

• Data engineering workflows
• Data science workflows
• Data processing
• Local experimentation
• Benchmarking tasks
• Validation pipelines

Data Processing & Analytics Experience

You should have practical hands-on experience with:

• Data processing
• Analytics workflows
• Machine learning-related workflows
• Experimental data validation

Machine Learning & Data Science Knowledge

A strong understanding of:

• Machine learning fundamentals
• Data science fundamentals
• Feature preparation
• Model-related workflows
• Evaluation logic

Why This Role Stands Out

This opportunity is appealing because it sits at the intersection of:

• Data Engineering
• Data Science
• Software Engineering
• Machine Learning
• AI Benchmarking

Rather than simply building dashboards or maintaining ETL pipelines, you’ll be working on real-world evaluation systems that help test how AI performs on technically demanding data tasks.

That means the work is likely to be:

• More intellectually challenging
• More research-oriented
• More engineering-heavy than standard analyst roles
• Valuable for professionals interested in AI infrastructure and model evaluation

Work Schedule & Flexibility

• Remote contract role
• 20–40 hours per week
• Hourly engagement

Application Process

The hiring process is simple and direct:

Step 1: Upload Your Resume

Submit your resume showing:

• Relevant data engineering / data science experience
• Python expertise
• ML or analytics workflow experience
• Complex codebase or benchmarking experience (if available)

Step 2: Technical Interview

Qualified candidates will proceed to a:

• Technical interview

This likely means you should be prepared to discuss:

• Python workflows
• Data pipeline design
• Reproducibility
• Benchmarking logic
• ML/data science fundamentals
• Real-world data engineering decisions

Compensation

The description confirms this is an:

• Hourly contract

However:

No exact hourly pay rate is listed in the provided details.

How to Apply

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