Data Scientist (Masters)

Alignerr; Wellington; On-site; NZD 144,000 - 287,000; Part time

Job summary

Alignerr is seeking Masters- or PhD-level data scientists to challenge and audit cutting-edge AI models, stress-testing reasoning and identifying blind spots. You will craft complex data science problems, provide rigorous ground-truth solutions in Python/R and SQL, and evaluate model outputs for correctness and efficiency.

This is a fully remote, hourly-contract role with 10–40 hours per week. You’ll work independently on flexible projects and contribute to ongoing AI initiatives at Alignerr.

Qualifications

• Pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a heavily quantitative field.
• Strong foundational knowledge across core areas — supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP.
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing.
• Naturally detail-oriented — you catch syntax errors, notation inconsistencies, and flawed statistical conclusions that others miss.
• Self-motivated and reliable when working independently without supervision.
• No prior AI or data annotation experience required.

Responsibilities

• Design Advanced Challenges — Craft complex, domain-spanning data science problems covering areas like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction
• Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as the definitive benchmark for AI responses
• Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
• Identify Reasoning Failures — Spot logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that meaningfully improves model behavior
• Work Independently — Complete task-based assignments asynchronously on your own schedule

Skills

Python

R

SQL

Statistical modeling

Machine learning

Education

Masters or PhD in Data Science / Statistics / Computer Science

Tools

Scikit-Learn

PyTorch

TensorFlow

Spark

Hadoop

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 Masters-level data scientists to challenge, audit, and refine cutting-edge AI models — stress-testing their reasoning, catching their blind spots, and helping them get smarter.

This is a fully remote, flexible contract role. No prior AI industry experience required — just deep, rigorous knowledge of data science and a sharp eye for technical accuracy.

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

What You’ll Do

• Design Advanced Challenges — Craft complex, domain-spanning data science problems covering areas like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction
• Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions — including Python/R scripts, SQL queries, and mathematical derivations — that serve as the definitive benchmark for AI responses
• Audit AI-Generated Code — Evaluate model outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and technical soundness
• Identify Reasoning Failures — Spot logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and deliver structured feedback that meaningfully improves model behavior
• Work Independently — Complete task-based assignments asynchronously on your own schedule

Who You Are

• Pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a heavily quantitative field
• Strong foundational knowledge across core areas — supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
• Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
• Naturally detail-oriented — you catch syntax errors, notation inconsistencies, and flawed statistical conclusions that others miss
• Self-motivated and reliable when working independently without supervision
• No prior AI or data annotation experience required

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 monitoring
• Background in academic research, technical writing, or peer review
• Exposure to a broad range of applied ML domains — computer vision, time series, recommender systems, etc.

Why Join Us

• Work directly on cutting-edge AI projects alongside the world's leading research labs
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, intellectually stimulating work
• Engage hands-on with industry-leading large language models at the frontier of AI development
• Potential for ongoing work and contract renewals as new projects launch