We are sharing a specialised part-time consulting opportunity for experienced Machine Learning Engineers and Applied ML Researchers with expertise in end-to-end modeling, dataset analysis, feature engineering, validation strategy, model evaluation, reference solution development, and technical quality review.
This role supports current and upcoming remote consulting opportunities focused on complex machine learning challenge design, applied modeling workflows, reference solution development, technical evaluation, reproducible documentation, and high-quality project execution. Selected professionals will design, solve, and review challenging machine learning tasks that reflect real-world ML development across multiple domains and data modalities.
Key Responsibilities
Professionals in this role may contribute to:
End-to-End Machine Learning Solution Development
- Develop complete machine learning solutions for challenging prediction and modeling problems
- Analyze datasets and define appropriate modeling approaches, validation strategies, and evaluation metrics
- Perform exploratory data analysis, feature engineering, data preprocessing, model training, tuning, and evaluation
- Work across tabular, text, image, time-series, recommendation, ranking, or other applied ML problem types
Reference Solutions & Technical Documentation
- Develop strong reference solutions using industry-standard machine learning techniques and best practices
- Document methodologies, assumptions, modeling choices, validation approaches, and evaluation results clearly
- Ensure solutions are accurate, reproducible, and technically well-structured
- Identify opportunities to improve model performance through systematic experimentation and iteration
ML Project Review & Evaluation
- Review and validate the technical quality of machine learning projects and deliverables
- Evaluate modeling choices, data preparation decisions, performance metrics, and experimental design
- Identify weak assumptions, data leakage risks, flawed validation, underdeveloped features, or unsupported modeling conclusions
- Provide clear written technical feedback that improves correctness, rigor, and reproducibility
Ideal Profile
Strong candidates may have:
- Master's degree, PhD, or equivalent advanced experience in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field
- 2+ years of hands-on experience developing, training, evaluating, and optimizing machine learning models in a professional or research setting
- Strong proficiency in Python and modern machine learning frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
- Demonstrated experi