Data Scientist (Masters)

Job summary

Design complex data science challenges and author rigorous reference solutions to evaluate AI model performance. Audit AI-generated code for technical accuracy and provide structured feedback to refine 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 systems reason through complex problems? We're looking for experienced data scientists to challenge, audit, and improve cutting-edge AI models — pushing them to their limits across domains like Bayesian inference, deep learning, and data pipeline design, then documenting failure modes so we can make these systems sharper and more reliable. This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge and a rigorous, analytical mindset. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Design Advanced Challenges — Create complex, domain-rich data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more 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 AI evaluation Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness Refine Model Reasoning — Identify logical failures in AI outputs — data leakage, overfitting, mishandled class imbalance — and deliver structured, actionable feedback that directly improves how these models think 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 Strong foundational knowledge across core data science domains — supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP Able to communicate complex algorithmic concepts and statistical findings clearly and concisely in writing Exceptionally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss No prior AI or annotation experience required Nice to Have Experience with data annotation, data quality evaluation, or AI output review Familiarity with production-level data science workflows — MLOps, CI/CD for models, or model monitoring Comfort working across multiple technical domains and problem types Why Join Us Work directly with industry-leading AI models at the frontier of research Fully remote and async — work when and where it suits you Freelance autonomy with meaningful, intellectually stimulating work High-impact contributions that directly influence how the next generation of AI reasons through data science problems Potential for ongoing contracts and expanded project opportunities as new work launches

What you’ll do

Design complex data science challenges and author rigorous reference solutions to evaluate AI model performance. Audit AI-generated code for technical accuracy and provide structured feedback to refine model reasoning.

Requirements

Candidates must be pursuing or hold a Master's or PhD in a quantitative field with strong foundational knowledge in data science domains. Exceptional attention to detail and the ability to communicate complex algorithmic concepts clearly are required.

Benefits

• Flexible schedule • 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
• Bayesian inference
• Deep learning
• Data pipeline design
• Hyperparameter optimization
• Dimensionality reduction
• NLP
• Big data technologies

Job areas

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