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 world's most advanced AI systems reason and solve problems? We're looking for skilled data scientists to challenge, audit, and refine cutting-edge AI models — pushing them to their limits and making them smarter in the process.
This is a fully remote, flexible contract role built for data scientists who love deep technical problem-solving. No prior AI industry experience needed — just strong domain knowledge and a sharp analytical mind.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Design Complex Challenges: Develop advanced data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — tasks that genuinely stress-test AI reasoning
Author Ground-Truth Solutions: Write rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as authoritative "golden responses" for model training
Audit AI-Generated Code: Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness
Refine Model Reasoning: Identify logical failures in AI thinking — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback that sharpens how models reason through data science problems
Document Failure Modes: Capture and communicate every edge case and reasoning gap, helping research teams harden model performance across real-world data scenarios
Who You Are
Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong focus on data analysis
Solid foundational knowledge in supervised and unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
Naturally detail-oriented — 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, or evaluation systems
Familiarity with production-level data science workflows such as MLOps or CI/CD for models
Exposure to model evaluation, benchmarking, or AI research environments
Why Join Us
Work directly with industry-leading AI research labs on cutting-edge model development
Fully remote and async — work when and where it suits you
Freelance autonomy with meaningful, intellectually stimulating task-based work
Engage hands-on with state-of-the-art large language models
Potential for ongoing contract renewals as new AI projects launch

Work arrangement
No