Data Scientist (Masters)

Data Scientist (Masters) — AI Data Trainer

About The Role

What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the worlds most advanced AI systems think and reason Were looking for data scientists with graduate-level training to stress-test cutting-edge AI models — designing the hardest problems you can imagine, building the gold-standard solutions, and identifying exactly where AI reasoning breaks down.

This is a fully remote, flexible contract role built for serious data scientists who want to work at the frontier of AI development — on their own schedule.

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

• Design Advanced Challenges — Create rigorous, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
• Build Ground-Truth Solutions — Author precise, 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 model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
• Sharpen AI Reasoning — Identify logical failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback that directly improves how models think
• Work Independently — Complete task-based assignments asynchronously, fully on your own schedule

• Pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field with strong emphasis on data analysis
• Deep foundational knowledge in supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical results clearly and concisely in writing
• Exceptionally detail-oriented — precise when checking code syntax, mathematical notation, and the validity of statistical conclusions
• Self-motivated and consistent when working independently
• No prior AI industry experience required

• Prior experience with data annotation, data quality evaluation, or AI output review
• Proficiency in production-level data science workflows — MLOps, CI/CD for models, or model monitoring
• Familiarity with academic or industry benchmarking methodologies
• Broad exposure across multiple data science subfields

• Work directly with industry-leading AI research labs on cutting-edge model development
• Fully remote and flexible — work when and where it suits you
• High agency and autonomy as an independent contractor
• Meaningful, intellectually stimulating work that pushes the boundaries of what AI can do
• Potential for ongoing contract renewals as new projects launch