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 models think and reason? We're looking for Masters-level data scientists to challenge, stress-test, and refine cutting-edge AI systems — helping ensure they reason correctly, write clean code, and handle complex problems with precision.

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

• Design complex data science challenges across domains like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction — pushing AI models to their limits
• Author rigorous ground-truth solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the gold standard for model evaluation
• Audit AI-generated code and outputs using libraries like Scikit-Learn, PyTorch, and Tensor Flow — assessing technical accuracy, efficiency, and correctness
• Identify and document reasoning failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback to sharpen model reasoning
• Work independently and 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 areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to write clearly and precisely about complex algorithmic concepts and statistical results for technical audiences
• Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
• Self-motivated and consistent when working independently
• No prior AI or data annotation experience required

• Experience with data annotation, data quality evaluation, or AI evaluation systems
• Proficiency in production-level data science workflows such as MLOps or CI/CD for models
• Familiarity with model benchmarking or technical content authoring
• Background spanning multiple data science subfields — the broader your expertise, the more impactful your contributions

• Work directly with industry-leading AI research labs on cutting-edge model development
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, task-based work
• Make a direct, tangible impact on how advanced AI models reason about data science
• Potential for ongoing work and contract extension as new projects launch