Data Science Expert - AI Content Specialist

Build rigorous data science evaluations

Challenge AI on machine learning, statistics and data engineering. The work combines difficult problem design, authoritative solutions and detailed audits of model generated analyses.

Technical responsibilities

• Design problems involving hyperparameter optimization, Bayesian inference, cross validation, dimensionality reduction and related topics.
• Write reference solutions with Python or R scripts, SQL queries and mathematical derivations.
• Review generated work using scikit-learn, PyTorch and TensorFlow for correctness, efficiency and sound practice.
• Identify data leakage, overfitting and incorrect treatment of imbalanced datasets.
• Explain statistical or logical weaknesses through structured feedback that improves model reliability.

Qualifications

A master's degree in progress or completed, or a PhD, is required in data science, statistics, computer science or a related quantitative field with substantial data analysis. Strong grounding in supervised or unsupervised learning, deep learning, Spark or Hadoop, or natural language processing is relevant.

You must explain algorithms and statistical results clearly in writing. Precision with code, mathematical notation and the validity of conclusions is essential.

Prior AI industry experience is not required. Annotation, data quality evaluation and AI assessment experience are useful additions, as are production data science workflows such as MLOps or model CI/CD.

Contract terms

The hourly contract is fully remote and asynchronous, with a commitment of ten to 40 hours weekly. Work combines independent technical assignments with a wider research and specialist network.

Ongoing work or renewal may be available as additional projects launch.