Data Scientist (Masters)

Job summary

Develop challenging data science problems and write rigorous reference solutions using code and mathematical derivations. Evaluate AI-generated code and reasoning, document errors such as data leakage and overfitting, and provide feedback to improve model performance.

Job details

Data Scientist (Masters) — AI Model 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 through complex problems? We're looking for data scientists with advanced degrees to challenge, audit, and refine cutting-edge AI models — helping them think more rigorously, reason more accurately, and perform at a higher level. This is a fully remote, flexible contract role. No prior AI industry experience required — just deep domain knowledge and a sharp analytical mind. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Design Advanced Challenges: Develop complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — problems that genuinely push AI reasoning to its limits Author Ground-Truth Solutions: Write rigorous, step-by-step technical solutions including Python and R scripts, SQL queries, and mathematical derivations that serve as authoritative reference answers Audit AI-Generated Code: Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing them for technical accuracy, efficiency, and correctness Identify and Document Failure Modes: Catch logical errors in AI reasoning such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback that improves model behaviour Refine Model Reasoning: Help shape how AI models approach statistical and algorithmic problems by documenting every meaningful failure and guiding iterative improvements Who You Are Currently pursuing or have completed a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus Solid foundational knowledge across core areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP Able to communicate highly technical concepts — algorithmic logic, statistical results, mathematical derivations — clearly and precisely in written form Naturally detail-oriented: you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss Self-directed and comfortable working independently on technical tasks No prior AI or data annotation experience required Nice to Have Prior experience with data annotation, data quality assurance, or model evaluation systems Proficiency in production-level data science workflows such as MLOps or CI/CD pipelines for machine learning models Familiarity with prompt engineering or working with large language models Experience writing technical documentation or academic-style explanations of complex methods Why Join Us Work directly with industry-leading AI research teams and language models at the frontier of the field Fully remote and flexible — work when and where it suits you, on your own schedule Freelance autonomy with the structure of meaningful, technically rigorous work Make a direct, measurable impact on how the next generation of AI understands and applies data science Potential for ongoing work and contract extension as new projects launch

What you’ll do

Develop challenging data science problems and write rigorous reference solutions using code and mathematical derivations. Evaluate AI-generated code and reasoning, document errors such as data leakage and overfitting, and provide feedback to improve model performance.

Requirements

Applicants must be pursuing or have completed a Master's or PhD in data science, statistics, computer science, or a quantitative field focused on data analysis. They should have strong foundational data science knowledge, communicate technical concepts clearly in writing, be detail-oriented, and work independently; prior AI industry experience is not required.

Benefits

• Flexible Schedule
• Fully Remote Work
• Freelance Autonomy
• Potential for Ongoing Work and Contract Extension

Listed skills

• Technical Documentation · Preferred
• 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
• Supervised Learning
• Unsupervised Learning
• Deep Learning
• Bayesian Inference
• Hyperparameter Optimization
• Cross-Validation
• Dimensionality Reduction
• Python
• R
• SQL
• Code Auditing
• Data Quality Assurance
• Technical Documentation

Job areas

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

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