Data Scientist (Masters)

Job summary

Design complex data science challenges and author rigorous ground-truth solutions to refine AI model reasoning. Audit AI-generated code for technical accuracy and document failure modes to improve model reliability.

Job details

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 problem-solve? We're looking for data scientists with graduate-level training to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and building the ground-truth solutions that make them smarter. This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep, rigorous command of data science and a sharp eye for technical accuracy. 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 Author Ground-Truth Solutions — Write rigorous, 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 AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness Sharpen AI Reasoning — Identify logical failures in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that directly improves how these models think Document Failure Modes — Systematically record how and where advanced language models break down on data science tasks, helping research teams harden model reliability Who You Are Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative discipline with a strong data focus Deeply fluent in core data science concepts: supervised and unsupervised learning, deep learning, statistical inference, and big data technologies Able to translate complex algorithmic concepts and statistical results into clear, precise written explanations Exceptionally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss Comfortable working independently and asynchronously without hand-holding No prior AI or data annotation experience required Nice to Have Experience with data annotation, data quality evaluation, or AI model assessment workflows Proficiency in production data science environments — MLOps, CI/CD for models, model monitoring Familiarity with NLP, computer vision, or large-scale distributed computing (Spark, Hadoop) Background in technical writing, research, or academic publishing Why Join Us Work directly with industry-leading AI research teams and cutting-edge language models Fully remote and flexible — structure your hours around your life Freelance autonomy with meaningful, intellectually stimulating work Contribute to AI development that shapes the future of data science reasoning at scale Potential for ongoing contract renewals as new projects launch

What you’ll do

Design complex data science challenges and author rigorous ground-truth solutions to refine AI model reasoning. Audit AI-generated code for technical accuracy and document failure modes to improve model reliability.

Requirements

Candidates must be pursuing or hold a Master's or PhD in a quantitative discipline with deep fluency in core data science concepts. Strong technical writing skills and the ability to work independently are essential for this role.

Listed skills

• SQL · Preferred
• Machine learning · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Statistical Inference
• Data Engineering
• Python
• R
• SQL
• Scikit-Learn
• PyTorch
• TensorFlow
• Supervised Learning
• Unsupervised Learning
• Deep Learning
• Big Data Technologies
• Data Annotation
• Technical Writing
• Mathematical Derivations

Job areas

• Data & Analytics
• Technology
• Science & Research
• Software
• Engineering