Data Scientist (Masters)

Job summary

Design complex data science challenges to stress-test AI models and author rigorous ground-truth solutions for model evaluation. Audit AI-generated outputs for technical accuracy and provide structured feedback to improve model reasoning.

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 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. This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge, sharp analytical instincts, and the ability to communicate technical concepts clearly. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do 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 TensorFlow — 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 Who You Are 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 Nice to Have 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 Why Join Us 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

What you’ll do

Design complex data science challenges to stress-test AI models and author rigorous ground-truth solutions for model evaluation. Audit AI-generated outputs for technical accuracy and provide structured feedback to improve model reasoning.

Requirements

Candidates must be pursuing or hold a Master's or PhD in a quantitative field with deep knowledge of core data science areas. Strong analytical instincts and the ability to communicate complex technical concepts clearly are essential.

Benefits

• Flexible schedule • Fully remote work • Freelance autonomy

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
• NLP
• Hyperparameter optimization
• Bayesian inference

Job areas

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