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 problem-solve? We're looking for Masters-level data scientists to challenge, evaluate, and refine cutting-edge AI models — stress-testing their reasoning on the hardest problems your field has to offer.

This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge and a sharp eye for technical accuracy.

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

• Design Advanced Challenges — Create rigorous 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 definitive reference answers
• Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and best practices
• Identify Reasoning Failures — Spot and document logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback to improve model behavior
• Work Independently — Complete task-based assignments asynchronously, fully on your own schedule

• Currently pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis
• Solid foundational knowledge across core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical findings clearly and concisely in writing
• Precise and detail-oriented — you notice when code logic, mathematical notation, or statistical conclusions don't hold up
• No prior AI or data annotation experience required

• Experience with data annotation, data quality assurance, or evaluation systems
• Familiarity with production-level data science workflows — MLOps, CI/CD for models, or model monitoring
• Background in academic research, technical writing, or peer review
• Hands-on experience with experiment tracking tools like MLflow or Weights & Biases

• Work directly with industry-leading AI research labs on genuinely cutting-edge models
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, high-impact technical work
• Engage deeply with topics at the frontier of machine learning and AI development
• Potential for ongoing work and contract extension as new projects launch