Data Scientist (Masters)

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 and solve problems? We're looking for Data Scientists with advanced degrees to challenge, evaluate, and refine cutting-edge AI models — exposing their blind spots, authoring gold-standard solutions, and making them genuinely smarter. This is a fully remote, flexible contract role. No prior AI industry experience required — just deep domain knowledge and a passion for rigorous, high-quality technical work. • Organization: Alignerr • Type: Hourly Contract • Location: Remote • Commitment: 10–40 hours/week What You'll Do • Design Advanced Challenges — Create complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more • Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as definitive reference answers • Audit AI-Generated Code — Evaluate outputs from models using libraries like Scikit-Learn, PyTorch, and TensorFlow for technical accuracy, efficiency, and correctness • Sharpen AI Reasoning — Identify logical failures in AI outputs — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback to improve model reasoning • Document Failure Modes — Systematically capture how and why models break down across domains like neural network architectures, statistical modeling, and data engineering pipelines Who You Are • Pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis • Solid foundational knowledge across 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 • Precise and detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning • No prior AI or annotation experience required Nice to Have • Experience with data annotation, data quality assurance, or model evaluation workflows • Proficiency in production-level data science practices — MLOps, CI/CD pipelines for models • Familiarity with prompt engineering or working directly with large language models • Background in academic or applied research Why Join Us • Work directly on cutting-edge AI projects alongside leading research labs • Fully remote and asynchronous — work when and where it suits you • Freelance autonomy with the structure of meaningful, technically challenging work • Engage hands-on with industry-leading large language models • Potential for ongoing work and contract extension as new projects launch • Make a direct, measurable impact on how AI reasons through the hardest problems in data science