Data Scientist (Masters)

Alignerr Corp.; Sydney; Remote; AUD 83,000 - 165,000; Full time

Job summary

Alignerr is seeking a Data Scientist (Masters) — AI Data Trainer to stress-test advanced AI models and craft gold-standard solutions. The fully remote, hourly-contract role focuses on designing challenges and refining AI reasoning.

You will author rigorous solutions, audit outputs with Scikit-Learn, PyTorch, and TensorFlow, and provide structured feedback to improve model behavior. Flexible schedule and independent work are emphasized.

Qualifications

• Pursuing or holding a Master’s or PhD in data science, statistics, computer science, or a quantitative field.
• Strong knowledge of supervised/unsupervised learning, deep learning, and statistical inference.
• Proficient coding in Python/R; comfortable with SQL and data manipulation.

Responsibilities

• Design advanced data science challenges across domains like hyperparameter optimization and Bayesian inference.
• Author ground-truth solutions with scripts, queries and derivations as benchmarks.
• Audit AI-generated code and analyses for accuracy and efficiency.
• Identify and document reasoning failures and provide structured feedback.

Skills

Masters/PhD DS

ML & Stats

Python/R/SQL

Academic writing

Independent worker

Education

Master's/PhD in Data Science

Tools

Scikit-Learn

PyTorch

TensorFlow

SQL

Job description

Data Scientist (Masters) — AI Data Trainer

About The Role

What if your expertise in machine learning, statistical modeling, and data engineering could directly shape how the world's most advanced AI systems think and reason? We're looking for experienced data scientists to stress-test cutting-edge AI models — designing challenges that expose their limits, authoring gold-standard solutions, and refining the reasoning that makes AI smarter.

This is a fully remote, flexible contract role built for people who love rigorous problem-solving and want their expertise to make a real impact on the future of AI.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Design advanced data science challenges across domains like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction — problems that push AI reasoning to its limits
• Author ground-truth solutions — rigorous, step-by-step technical responses including Python/R scripts, SQL queries, and mathematical derivations that serve as the definitive benchmark for AI performance
• Audit AI-generated code and analysis using libraries like Scikit-Learn, PyTorch, and TensorFlow — evaluating outputs for technical accuracy, efficiency, and correctness
• Identify and document reasoning failures — spotting issues like data leakage, overfitting, improper handling of imbalanced datasets, or flawed statistical conclusions, then providing structured feedback to improve model behavior
• Work independently and asynchronously on task-based assignments — fully on your own schedule

Who You Are

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a heavily quantitative field
• Deeply knowledgeable in core data science areas — supervised/unsupervised learning, deep learning, statistical inference, or big data technologies (Spark, Hadoop)
• Able to communicate complex algorithmic and statistical concepts clearly and precisely in writing
• Meticulous with code syntax, mathematical notation, and the validity of statistical conclusions
• Self-directed and reliable when working independently
• No prior AI or machine learning training experience required

Nice to Have

• Experience with data annotation, data quality evaluation, or model assessment workflows
• Proficiency in production-level data science practices — MLOps, CI/CD for models, or model monitoring
• Familiarity with NLP, computer vision, or large-scale data pipelines
• Background in academic research, technical writing, or peer review

Why Join Us

• Work directly alongside industry-leading AI research labs on genuinely frontier problems
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, high-impact technical work
• Engage hands-on with state-of-the-art language models and shape how they reason about data science
• Potential for ongoing work and contract renewals as new projects launch