Data Scientist (Masters)

Alignerr Corp.; Toronto; Remote; CAD 90,000 - 130,000; Full time

Job summary

Alignerr Corp. is seeking a Data Scientist (Masters) — AI Data Trainer for a fully remote contract role. You will design data science challenges across domains, author ground-truth solutions in Python/R and SQL, and audit AI-generated code to ensure accuracy and quality.

Masters-level expertise in ML, statistics, and data engineering is required, with clear communication to technical audiences. Remote, flexible schedule, potential for ongoing work and contract extensions.

Qualifications

• Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field with strong data analysis
• Strong knowledge in supervised/unsupervised learning, deep learning, NLP
• Experience with big data technologies (Spark/Hadoop) and Python/R, SQL skills
• Ability to communicate complex algorithmic concepts clearly
• Detail-oriented with careful code and statistical reasoning

Responsibilities

• Design complex data science challenges across domains (e.g., hyperparameter optimization, Bayesian inference, cross-validation, dimensionality reduction)
• Author rigorous ground-truth solutions with Python/R, SQL, and mathematical derivations
• Audit AI-generated code and outputs using Scikit-Learn, PyTorch, TensorFlow for accuracy and efficiency
• Identify data leakage, overfitting, and imbalanced datasets; provide structured feedback
• Work independently and asynchronously on a flexible schedule

Job description

Data Scientist (Masters) — AI Data 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 models think and reason? We're looking for Masters-level data scientists to challenge, stress-test, and refine cutting-edge AI systems — helping ensure they reason correctly, write clean code, and handle complex problems with precision.

This is a fully remote, flexible contract role. No prior AI industry experience needed — just deep domain knowledge, sharp analytical instincts, and the ability to communicate technical concepts clearly.

What You'll Do

• Design complex data science challenges across domains like hyperparameter optimization, Bayesian inference, cross-validation strategies, and dimensionality reduction — pushing AI models to their limits
• Author rigorous ground-truth solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the gold standard for model evaluation
• Audit AI-generated code and outputs using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing technical accuracy, efficiency, and correctness
• Identify and document reasoning failures such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback to sharpen model reasoning
• Work independently and asynchronously — fully on your own schedule

Who You Are

• Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a strong emphasis on data analysis
• Deeply knowledgeable in core areas such as supervised and unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
• Able to write clearly and precisely about complex algorithmic concepts and statistical results for technical audiences
• Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
• Self-motivated and consistent when working independently
• No prior AI or data annotation experience required

Nice to Have

• Experience with data annotation, data quality evaluation, or AI evaluation systems
• Proficiency in production-level data science workflows such as MLOps or CI/CD for models
• Familiarity with model benchmarking or technical content authoring
• Background spanning multiple data science subfields — the broader your expertise, the more impactful your contributions

Why Join Us

• Work directly with industry-leading AI research labs on cutting-edge model development
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with the structure of meaningful, task-based work
• Make a direct, tangible impact on how advanced AI models reason about data science
• Potential for ongoing work and contract extension as new projects launch