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

Work arrangement
Yes