Data Scientist (Masters)

Data Scientist (Masters) — AI Data TrainerAbout The RoleWhat if your expertise in machine learning, statistical inference, and data engineering could directly shape how the world's most advanced AI systems reason and problem-solve? We're looking for Masters-level data scientists to challenge, evaluate, and refine cutting-edge AI models — stress-testing their reasoning on the hardest problems your field has to offer.This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge and a sharp eye for technical accuracy.Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: 10–40 hours/weekWhat You'll DoDesign Advanced Challenges — Create rigorous data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and moreAuthor Ground-Truth Solutions — Write precise, step-by-step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as definitive reference answersAudit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and best practicesIdentify Reasoning Failures — Spot and document logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback to improve model behaviorWork Independently — Complete task-based assignments asynchronously, fully on your own scheduleWho You AreCurrently pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysisSolid foundational knowledge across core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLPAble to communicate complex algorithmic concepts and statistical findings clearly and concisely in writingPrecise and detail-oriented — you notice when code logic, mathematical notation, or statistical conclusions don't hold upNo prior AI or data annotation experience requiredNice to HaveExperience with data annotation, data quality assurance, or evaluation systemsFamiliarity with production-level data science workflows — MLOps, CI/CD for models, or model monitoringBackground in academic research, technical writing, or peer reviewHands-on experience with experiment tracking tools like MLflow or Weights & BiasesWhy Join UsWork directly with industry-leading AI research labs on genuinely cutting-edge modelsFully remote and flexible — work when and where it suits youFreelance autonomy with the structure of meaningful, high-impact technical workEngage deeply with topics at the frontier of machine learning and AI developmentPotential for ongoing work and contract extension as new projects launch