Data Scientist (Masters) — AI Model 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 through complex problems? We're looking for data scientists with advanced degrees to challenge, audit, and refine cutting‑edge AI models — helping them think more rigorously, reason more accurately, and perform at a higher level.

Organization: Alignerr

Type: Hourly Contract

Location: Remote

Commitment: 10–40 hours/week

What You'll Do

Design Advanced Challenges: Develop complex, domain‑specific data science problems spanning hyperparameter optimization, Bayesian inference, cross‑validation strategies, dimensionality reduction, and more — problems that genuinely push AI reasoning to its limits

Author Ground‑Truth Solutions: Write rigorous, step‑by‑step technical solutions including Python and R scripts, SQL queries, and mathematical derivations that serve as authoritative reference answers

Audit AI‑Generated Code: Evaluate AI outputs using libraries like Scikit‑Learn, PyTorch, and TensorFlow — assessing them for technical accuracy, efficiency, and correctness

Identify and Document Failure Modes: Catch logical errors in AI reasoning such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback that improves model behaviour

Refine Model Reasoning: Help shape how AI models approach statistical and algorithmic problems by documenting every meaningful failure and guiding iterative improvements

Who You Are





Currently pursuing or have completed a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong data analysis focus

Solid foundational knowledge across core areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP

Able to communicate highly technical concepts — algorithmic logic, statistical results, mathematical derivations — clearly and precisely in written form

Naturally detail‑oriented: you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss

Self‑directed and comfortable working independently on technical tasks

No prior AI or data annotation experience required

Nice to Have

Prior experience with data annotation, data quality assurance, or model evaluation systems

Proficiency in production‑level data science workflows such as MLOps or CI/CD pipelines for machine learning models

Familiarity with prompt engineering or working with large language models

Experience writing technical documentation or academic‑style explanations of complex methods

Why Join Us

Work directly with industry‑leading AI research teams and language models at the frontier of the field

Fully remote and versatile — work when and where it suits you, on your own schedule

Freelance autonomy with the structure of meaningful, technically rigorous work

Make a direct, measurable impact on how the next generation of AI understands and applies data science

Potential for ongoing work and contract extension as new projects launch

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📌 Data Scientist (Masters) (Alberta)
🏢 Alignerr
📍 Alberta