Data Scientist (Masters)

What if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason about data? We're looking for experienced data scientists to challenge, evaluate, and improve cutting-edge AI models — exposing their blind spots, correcting their logic, and building the ground-truth solutions that make them smarter. This is a fully remote, flexible contract role.

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

Responsibilities

• Design Advanced Challenges — Create complex, real-world data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
• 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 reference answers for AI training
• Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
• Identify Reasoning Failures — Catch logical errors in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback that improves how models think
• Work Independently — Complete task-based assignments asynchronously on your own schedule

Requirements

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with strong emphasis on data analysis
• Deeply fluent in core ML concepts — supervised/unsupervised learning, deep learning, probabilistic modeling, and statistical inference
• Comfortable working across big data technologies (Spark, Hadoop) and/or NLP frameworks
• Able to communicate complex algorithmic concepts and statistical results clearly in writing
• Precise and thorough — you catch errors in code syntax, mathematical notation, and statistical conclusions
• No prior AI training or annotation experience required

Nice to Have

• Experience with data annotation, data quality pipelines, or model evaluation systems
• Proficiency in production-level data science workflows — MLOps, CI/CD for models, or similar
• Background in academic research or technical writing

Benefits

• Work directly on cutting-edge AI projects alongside world-leading research labs
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with meaningful, intellectually stimulating work
• Direct engagement with the most advanced large language models being built today
• Potential for ongoing contracts and expanded project opportunities as new work launches