Data Scientist (Masters)

About the Role

What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason through complex problems? We're looking for data scientists with graduate-level training to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and building them into more reliable, rigorous tools.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, hands-on command of data science and the ability to think critically about how models reason.

What You'll Do

• Design Advanced Challenges — Develop complex, domain-specific data science problems spanning 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 the gold standard for evaluating AI outputs
• Audit AI-Generated Code — Evaluate AI-produced code using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
• Sharpen AI Reasoning — Identify logical failures in AI outputs — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that improves how models think
• Document Failure Modes — Systematically record where and how AI reasoning breaks down to help research teams harden model performance

Who You Are

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis
• Solid foundational knowledge across core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical findings clearly and concisely in writing
• Naturally precise — you catch errors in code syntax, mathematical notation, and statistical logic that others overlook
• Self-directed and reliable when working independently on task-based assignments
• No prior AI or data annotation experience required

Nice to Have

• Prior experience with data annotation, data quality assurance, or AI evaluation systems
• Familiarity with production-level data science workflows — MLOps, CI/CD for models, or similar
• Hands-on experience across multiple modelling domains or industry verticals
• Background in academic research or technical writing

Why Join Us

• Work directly with industry-leading AI research labs on frontier model development
• Fully remote and asynchronous — work when and where it suits you
• Freelance autonomy with meaningful, intellectually engaging work
• High-impact contributions: your expertise directly shapes how advanced AI systems reason about data science
• Potential for ongoing contracts and project renewals as new AI initiatives launch