Data Scientist (Masters)

Data Scientist (Masters) — AI Data TrainerAbout 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 respond? We're looking for Masters-level data scientists to challenge, audit, and improve cutting-edge AI models — exposing their blind spots and helping build more intelligent, reliable systems. This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, battle-tested knowledge of data science and the ability to communicate it precisely in writing.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Design Advanced Challenges — Create complex, domain-spanning data science problems covering hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — tasks that genuinely stress-test AI reasoning
• Author Ground-Truth Solutions — Produce rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as authoritative "golden responses"
• Audit AI-Generated Code — Evaluate AI outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow, assessing technical accuracy, efficiency, and correctness
• Refine AI Reasoning — Identify logical failures in AI outputs — data leakage, overfitting, mishandled class imbalance — and deliver structured feedback that improves how models think through data problems
• Document Failure Modes — Systematically record how and why advanced language models fail on technical tasks, contributing directly to model hardening efforts

Who You Are

• Pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a heavily quantitative field
• Strong foundational knowledge across core areas — supervised and unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
• Exceptionally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
• Self-motivated and reliable when working independently and asynchronously
• No prior AI or data annotation experience required

Nice to Have

• Prior experience with data annotation, data quality workflows, or model evaluation systems
• Proficiency in production-level data science environments — MLOps, CI/CD for models, or similar
• Familiarity with prompt engineering or working alongside large language models
• Broad cross-domain knowledge spanning multiple subfields of data science or statistics

Why Join Us

• Work directly with industry-leading AI research labs on genuinely frontier problems
• Fully remote and flexible — work on your own schedule, from anywhere in the world
• Freelance autonomy with the structure of meaningful, technically challenging work
• Make a direct, traceable impact on how next-generation AI models reason through complex data science problems
• Potential for ongoing contract renewals as new projects launch