Data Scientist (Masters)

About The Role

What if your deep knowledge of machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason and problem-solve?

We're looking for data scientists with graduate-level expertise to help train and evaluate cutting-edge AI models. You'll design complex technical challenges, author rigorous solutions, and audit AI-generated code — exposing model weaknesses and pushing the boundaries of what AI can reason through.

This is a fully remote, flexible contract role. No prior AI experience needed — just a strong command of data science and a sharp eye for technical precision.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

• Design Advanced Challenges: Develop complex, domain-spanning data science problems — covering hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
• Author Ground-Truth Solutions: Write rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as definitive reference answers
• Audit AI-Generated Code: Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
• Refine AI Reasoning: Identify logical failures in AI outputs — such as data leakage, overfitting, or improper handling of imbalanced datasets — and provide structured, actionable feedback to improve model reasoning
• Work Independently: Complete task-based assignments asynchronously on your own schedule

• Currently pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
• Strong foundational knowledge across core data science domains — supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing
• Highly detail-oriented when it comes to code syntax, mathematical notation, and the validity of statistical conclusions
• Self-motivated and reliable when working independently
• No prior AI training or annotation experience required

• Experience with data annotation, data quality evaluation, or model evaluation workflows
• Familiarity with production-level data science practices — MLOps, CI/CD for models, or similar
• Exposure to academic or applied research in machine learning or statistics
• Prior work in technical writing, code review, or curriculum design

• Work directly alongside leading AI research labs on frontier model development
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with consistent, meaningful, technically engaging work
• Build a portfolio of high-impact AI training contributions at the cutting edge of the field
• Potential for ongoing contracts and expanded project opportunities as new work launches