Data Scientist; Masters - AI Data Trainer

Data Scientist (Masters) - AI Data Trainer

This range is provided by Alignerr. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$40.00/hr - $80.00/hr

Location:

Remote

At Alignerr, we partner with the world’s leading AI research teams and labs to build and train cutting‑edge AI models. You’ll challenge advanced language models on topics like machine learning theory, statistical inference, neural network architectures, and data engineering pipelines—documenting every failure mode so we can harden model reasoning.

Organization: Alignerr Position: Data Scientist (Masters) - AI Data Trainer Type: Hourly Contract Compensation: $40–$80 /hour Location: Remote Commitment: 10–40 hours/week

• Develop Complex Problems:
  Design advanced data science challenges across domains like hyperparameter optimization, Bayesian inference, cross‑validation strategies, and dimensionality reduction.
• Author Ground‑Truth Solutions:
  Create rigorous, step‑by‑step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as "golden responses."
• Technical Auditing:
  Evaluate AI‑generated code (using libraries like Scikit‑Learn, PyTorch, or Tensor Flow), data visualizations, and statistical summaries for technical accuracy and efficiency.
• Refine Reasoning:
  Identify logical fallacies in AI reasoning—such as data leakage, overfitting, or improper handling of imbalanced datasets—and provide structured feedback to improve the model's "thinking" process.

• Advanced Degree:
  Masters (pursuing or completed) or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a heavy emphasis on data analysis.
• Domain Expertise:
  Strong foundational knowledge in core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
• Analytical Writing:
  The ability to communicate highly technical algorithmic concepts and statistical results clearly and concisely in written form.
• Attention to Detail:
  High level of precision when checking code syntax, mathematical notation, and the validity of statistical conclusions.
• No AI experience required

• Prior experience with data annotation, data quality, or evaluation systems
• Proficiency in production‑level data science workflows (e.g., MLOps, CI/CD for models).

• Excellent compensation with location‑independent flexibility.
• Direct engagement with industry‑leading LLMs.
• Contractor advantages: high agency, agility, and international reach.
• More opportunities for contracting renewals.

• Submit your resume
• Complete a short screening
• Project matching and onboarding

PS:
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