Alignerr
Data Scientist (Masters)
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Data Scientist (Masters)
Data Scientist (Masters) — AI Data Trainer
About the Role
What if your deep knowledge of machine learning, statistical inference, and data engineering could directly shape how the world's most powerful AI systems think and reason?
We're looking for data scientists with advanced training to challenge, audit, and sharpen cutting-edge AI models — exposing their blind spots, authoring gold-standard solutions, and helping them reason better across some of the most technically demanding problems in the field.
Organization & Commitment
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Flexible Commitment: 10–40 hours/week
What You'll Do
- Design Advanced Challenges — Create complex, domain-specific data science problems spanning areas like:
- Hyperparameter optimization
- Bayesian inference
- Cross-validation strategies
- Dimensionality reduction
- Author Ground-Truth Solutions — Write rigorous, step-by-step technical solutions, including:
- Python/R scripts
- SQL queries
- Mathematical derivations (Serving as the definitive benchmark for AI responses)
- Audit AI-Generated Code — Evaluate model outputs using libraries like:
- Scikit-Learn
- PyTorch
- TensorFlow (For technical accuracy, efficiency, and correctness)
- Sharpen AI Reasoning —
- Identify logical flaws (e.g., data leakage, overfitting, mishandled imbalanced datasets)
- Provide structured feedback to improve model performance
- Document Failure Modes — Systematically record where and how AI models break down so research teams can build more robust, trustworthy systems.
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Who You Are
You should meet the following requirements:
- Pursuing or holding a Master’s or PhD in:
- Data Science
- Statistics
- Computer Science
- Any heavily quantitative field
- Strong foundational knowledge of:
- Supervised/unsupervised learning
- Deep learning
- Big data technologies (Spark/Hadoop)
- NLP (Nice to have for the role)
- Ability to:
- Communicate complex algorithmic concepts and statistical results clearly and concisely in writing
- Maintain exceptional detail orientation (e.g., catching errors in code syntax, mathematical notation, and statistical reasoning)
- Must be:
- Self-directed
- Comfortable working independently (no hand-holding required)
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Prior AI or data annotation experience is not required.
Nice to Have (Additional Strengths)
- Experience with data annotation, data quality evaluation, or model assessment workflows
- Proficiency in production-level data science practices:
- MLOps
- CI/CD for model deployment
- Experiment tracking
- Familiarity with a broad range of ML frameworks and tooling
Why Join Us
- Work directly with industry-leading AI research labs on genuinely frontier problems
- Fully remote and flexible — no fixed schedule; work when and where it suits you
- Freelance autonomy with intellectual stimulation and meaningful technical work
- High agency environment — your expertise directly influences the quality of work produced
- Potential for ongoing contracts and expanded roles as new initiatives launch
“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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