Alignerr
Data Scientist (Masters)
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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
- Mathematical derivations (serving as definitive reference answers)
- Audit AI-Generated Code — Evaluate outputs from models (using libraries like Scikit-Learn, PyTorch, TensorFlow) for:
- Technical accuracy
- Efficiency
- Correctness
- Sharpen AI Reasoning — Identify logical failures in AI outputs, such as:
- Data leakage
- Overfitting
- Improper handling of imbalanced datasets Then provide structured feedback to improve model reasoning.
- Document Failure Modes — Systematically capture how and why models break down across:
- Neural network architectures
- Statistical modeling
- Data engineering pipelines
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Responsibilities
Requirements
Minimum Qualifications:
- Holding or pursuing 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 as expertise in this area)
- Ability to clearly and concisely communicate:
- Complex algorithmic concepts
- Statistical findings (in writing)
- Precision and detail-oriented—you catch errors in:
- Code syntax
- Mathematical notation
- Statistical reasoning
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No prior experience required:
- In AI or annotation practices.
Nice to Have
- Experience with:
- Data annotation
- Data quality assurance
- Model evaluation workflows
- Proficiency in production-level data science (such as MLOps and CI/CD pipelines for models).
- Familiarity with:
- Prompt engineering
- Direct work 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 structure from meaningful, technically demanding work.
- Engage hands-on with industry-leading large language models.
- Potential for ongoing work and contract extensions as new projects arise.
- Make a direct, measurable impact on how AI reasons through the hardest problems in data science.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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