Data Scientist (Masters)

Data science evaluation

Create technical challenges and assess how AI reasons through machine learning, statistical inference, and data engineering. The work emphasizes rigorous solutions and precise documentation of model failures.

• Develop problems involving hyperparameter tuning, Bayesian inference, cross-validation, dimensionality reduction, and related topics.
• Write authoritative solutions with justified steps, Python or R scripts, SQL queries, and mathematical derivations.
• Review output using Scikit-Learn, PyTorch, TensorFlow, or related libraries for technical validity, efficiency, and correctness.
• Identify data leakage, overfitting, poor handling of imbalanced datasets, and other reasoning mistakes.
• Record unusual cases and logical gaps so researchers can improve performance on practical data scenarios.

Required expertise

You must be pursuing or hold a master's or PhD in Data Science, Statistics, Computer Science, or a quantitative subject with a substantial data analysis focus. Strong foundations are needed in areas such as supervised and unsupervised learning, deep learning, Spark or Hadoop, or NLP.

You should explain algorithmic concepts and statistical results clearly in writing. Careful review of syntax, mathematical notation, and conclusions is essential.

Previous AI industry, training, or annotation experience is not required.

Helpful background

Annotation, data quality, evaluation systems, MLOps, and CI/CD for models are useful experience. Model evaluation, benchmarking, or work in AI research settings is also welcome.

Contract arrangement

This fully remote hourly contract offers flexible asynchronous work for 10 to 40 hours per week. Assignments provide hands-on exposure to advanced language models and may lead to further contract renewals as projects begin.