Data Scientist (Masters)

Job summary

Create advanced data science challenges and rigorous reference solutions, including code and mathematical derivations, to train and evaluate AI models. Review AI-generated code and statistical outputs, identify reasoning errors, and provide actionable feedback to improve model performance.

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 reasons through complex problems? We're looking for skilled data scientists to join Alignerr's network of expert AI trainers — working alongside leading AI research labs to challenge, evaluate, and improve cutting-edge language models on deep technical topics. This is a fully remote, flexible contract role. No prior AI industry experience required — just a strong command of data science fundamentals and the ability to communicate technical ideas with precision. 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 — stress-testing model reasoning at the frontier Author Ground-Truth Solutions: Build rigorous, step-by-step reference solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the gold standard for model training Audit AI-Generated Code: Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing technical accuracy, efficiency, and correctness of data visualizations and statistical summaries Refine Model Reasoning: Identify logical flaws in AI outputs — such as data leakage, overfitting, or improper handling of imbalanced datasets — and provide structured, actionable feedback that improves how models think Who You Are Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with heavy emphasis on data analysis Strong foundational knowledge in core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP Able to communicate complex algorithmic concepts and statistical results clearly and concisely in written form Precise and detail-oriented when reviewing code syntax, mathematical notation, and the validity of statistical conclusions No prior AI training or annotation experience required Nice to Have Prior experience with data annotation, data quality assessment, or evaluation systems Proficiency in production-level data science workflows such as MLOps or CI/CD for models Familiarity with model evaluation frameworks or benchmarking methodologies Why Join Us Work directly with industry-leading AI language models at the cutting edge of the field Fully remote and flexible — work when and where it suits you, on your own schedule Freelance autonomy: high agency, global reach, and task-based structure that respects your expertise Contribute to meaningful work that shapes how AI reasons through the world's hardest technical problems Potential for ongoing contract renewals as new AI projects launch

What you’ll do

Create advanced data science challenges and rigorous reference solutions, including code and mathematical derivations, to train and evaluate AI models. Review AI-generated code and statistical outputs, identify reasoning errors, and provide actionable feedback to improve model performance.

Requirements

Applicants must be pursuing or hold a master's degree or PhD in data science, statistics, computer science, or another quantitative field with a strong data analysis focus. They should have foundational knowledge of data science and be able to communicate technical concepts clearly and review code and statistical conclusions precisely; prior AI training experience is not required.

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
• Natural Language Processing
• Bayesian Inference
• Hyperparameter Optimization

Job areas

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

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