Data Scientist (Masters)

About The Role

What if your expertise in machine learning, statistical modeling, and data engineering could directly shape how the world's most advanced AI systems reason and solve problems? We're looking for experienced data scientists to challenge, audit, and improve cutting-edge AI models — pushing them to their limits and making them smarter in the process.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, proven knowledge 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 rigorous, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
• Author Ground-Truth Solutions: Write precise, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as authoritative reference answers
• Audit AI-Generated Code: Evaluate AI-produced code using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
• Refine AI Reasoning: Identify flaws in model reasoning — such as data leakage, overfitting, or mishandled class imbalance — and provide structured, actionable feedback to improve how AI thinks through problems
• Document Failure Modes: Systematically record model shortcomings across areas like neural network architecture, statistical inference, and data engineering pipelines

• Currently pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
• Deeply knowledgeable in core data science domains: supervised/unsupervised learning, deep learning, NLP, big data technologies (Spark, Hadoop), or similar
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
• Meticulous when checking code syntax, mathematical notation, and the validity of statistical conclusions
• Self-directed and reliable — comfortable working independently on technical tasks without hand-holding
• No prior AI training or annotation experience required

• Prior experience with data annotation, data quality evaluation, or model evaluation systems
• Familiarity with production-level data science workflows — MLOps, CI/CD for models, or similar
• Experience reviewing or auditing technical work in academic or professional settings

• Work directly with industry-leading AI research labs and cutting-edge language models
• Fully remote and asynchronous — work when and where it suits you
• Flexible contractor arrangement with high autonomy and global accessibility
• Meaningful, intellectually stimulating work that directly influences the future of AI
• Potential for ongoing contract renewals as new projects launch