Alignerr
Data Scientist (Masters)
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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 think and reason?
We're looking for data scientists with advanced training to join Alignerr's AI training program — working hands-on with cutting-edge language models to stress-test their reasoning, expose their blind spots, and help build AI that actually gets the hard stuff right.
This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, rigorous knowledge of data science and the ability to communicate it with precision.
- 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 reference solutions including Python/R scripts, SQL queries, and mathematical derivations that set the standard for correct AI responses
- Audit AI-Generated Code — Evaluate outputs from models using Scikit-Learn, PyTorch, TensorFlow, and other major libraries for technical accuracy, efficiency, and soundness
- Sharpen AI Reasoning — Identify and document logical failures in AI outputs — data leakage, overfitting, improper handling of class imbalance — and provide structured feedback that improves how models think
- Document Failure Modes — Systematically record where and how AI reasoning breaks down so research teams can harden model behavior at scale
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Who You Are
- Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with heavy emphasis on data analysis
- Deeply grounded in core data science: supervised and unsupervised learning, deep learning, statistical inference, and big data technologies like Spark or Hadoop
- Comfortable writing rigorous technical solutions and explaining complex algorithmic concepts clearly in writing
- Precise and detail-oriented — you catch errors in code syntax, mathematical notation, and statistical conclusions that others miss
- Self-directed and reliable when working independently on an asynchronous schedule
- No prior AI or data annotation experience required
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Nice to Have
- Experience with data annotation, data quality workflows, or evaluation systems
- Familiarity with production-level data science practices — MLOps, CI/CD for models, or model deployment pipelines
- Exposure to NLP, computer vision, or other applied ML domains
Why Join Us
- Work directly with industry-leading AI language models on technically meaningful problems
- Fully remote and flexible — work when and where it suits you
- High autonomy contractor arrangement with international reach
- Contribute to AI development that shapes how the technology understands data science at its frontier
- Potential for ongoing work and contract renewal as new projects launch
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Jessica, London
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