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 powerful AI systems think and reason?

We're looking for data scientists with advanced training to challenge, audit, and sharpen cutting-edge AI models — exposing their blind spots, authoring gold-standard solutions, and helping them reason better across some of the most technically demanding problems in the field.

This is a fully remote, flexible contract role. No prior AI industry experience required — just serious data science expertise and a sharp analytical mind.

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

• Design Advanced Challenges — Create complex, domain-specific data science problems spanning areas like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction
• Author Ground-Truth Solutions — Write rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as the definitive benchmark for AI responses
• Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
• Sharpen AI Reasoning — Identify logical flaws in AI thinking — data leakage, overfitting, mishandled imbalanced datasets — and provide structured feedback to improve model performance
• Document Failure Modes — Systematically record where and how AI models break down so research teams can build more robust, trustworthy systems

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a heavily quantitative field
• Strong foundational knowledge in core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
• Exceptionally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
• Self-directed and comfortable working independently without hand-holding
• No prior AI or data annotation experience required

• Experience with data annotation, data quality evaluation, or model assessment workflows
• Proficiency in production-level data science practices — MLOps, CI/CD for model deployment, experiment tracking
• Familiarity with a broad range of ML frameworks and tooling

• Work directly with industry-leading AI research labs on genuinely frontier problems
• Fully remote and flexible — work when and where it suits you, on your own schedule
• Freelance autonomy with meaningful, intellectually stimulating technical work
• High agency environment — your expertise drives the quality of the work
• Potential for ongoing contracts and expanded project opportunities as new initiatives launch