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 and solve problems? We're looking for Data Scientists with advanced degrees to challenge, audit, and improve cutting-edge AI models — exposing their blind spots and building ground-truth solutions that make them smarter.

This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge and a rigorous, analytical mind.

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

• Design Advanced Challenges: Craft complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — problems that push AI models to their limits
• Author Ground-Truth Solutions: Develop 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: Critically evaluate AI outputs — including code written with Scikit-Learn, PyTorch, TensorFlow, and similar libraries — for technical correctness, efficiency, and best practices
• Refine AI Reasoning: Identify and document failure modes in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets, flawed statistical conclusions — and provide structured feedback that directly improves model intelligence
• Work Independently: Complete task-based assignments asynchronously, fully on your own schedule

• 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
• Deeply knowledgeable in core data science domains: supervised and unsupervised learning, deep learning, statistical inference, or big data technologies (Spark, Hadoop)
• Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing
• Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical logic that others miss
• Self-motivated and consistent when working independently
• No prior AI or annotation experience required

• Experience with data annotation, data quality assurance, or model evaluation workflows
• Familiarity with production-level data science practices — MLOps, CI/CD pipelines for models, or experiment tracking
• Background in NLP, computer vision, or other specialized machine learning domains
• Prior work in academic research, technical writing, or peer review

• Work directly with industry-leading AI models and cutting-edge research labs
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, high-impact technical work
• Make a tangible contribution to how AI understands and applies data science at scale
• Potential for ongoing contract renewals as new projects launch