Remote | Applied Scientist — $245–$280/hour

24-Mag•New York, NY

14d•Remote

About The Position

This specialised remote consulting role is for experienced Applied Scientists and Data Science Professionals contributing technical expertise to AI training and evaluation initiatives. The work combines applied data science, statistical reasoning, experimental methodology, computational modelling, and structured AI evaluation to help improve the accuracy and analytical capabilities of advanced AI systems. Prior experience with AI training projects is not required. The selected professional will conduct statistical analyses and quantitative research, develop and evaluate machine learning models, build reproducible Python-based analytical workflows, and assess AI-generated solutions to complex quantitative problems. Deliverables may include reproducible Python code, analytical reports, experimental analyses, and reference solutions. Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party.

Requirements

Strong professional or research experience in applied data science, machine learning, statistics, or quantitative analytics.
Strong Python programming skills, including practical proficiency with Pandas and scikit-learn.
Demonstrated experience conducting statistical analyses and interpreting complex quantitative datasets.
Strong understanding of probability, statistical inference, experimental design, and hypothesis testing.
Practical experience developing, training, tuning, and evaluating machine learning models.
Proficiency in data modelling, feature engineering, data transformation, and analytical workflow development.
Experience managing and analysing structured and unstructured datasets.
Ability to identify methodological errors, statistical inconsistencies, and weaknesses in quantitative reasoning.
Strong understanding of model evaluation techniques, validation metrics, and reproducible experimentation.
Experience developing clear, well-documented Python solutions and analytical reports.
Ability to translate complex business or research questions into rigorous analytical approaches.
Strong analytical reasoning, mathematical problem-solving, critical-thinking, and technical research capabilities.
Excellent written and verbal communication skills, including the ability to explain sophisticated technical concepts clearly.
Exceptional attention to detail and commitment to accurate, transparent, well-documented analytical work.
Ability to work independently and contribute effectively to specialised remote research assignments.
Work must be completed without using confidential or proprietary information belonging to an employer, client, institution, or other third party.

Nice To Haves

At least one year of experience at a leading technology, finance, or research organisation.
Experience collaborating with cross-functional teams or contributing to collaborative data science projects.
Familiarity with reproducible research practices, peer review, and structured technical evaluation.

Responsibilities

Design and execute rigorous statistical analyses of complex datasets, applying statistical inference, probability, hypothesis testing, and quantitative modelling.
Evaluate analytical assumptions, methods, interpretations, and quantitative findings for accuracy and methodological consistency.
Identify meaningful patterns, relationships, and trends in structured and unstructured datasets, and communicate justified conclusions and recommendations.
Develop, train, tune, and validate machine learning models using appropriate supervised and unsupervised learning methods.
Evaluate model performance, identify limitations and implementation errors, and seek opportunities to improve predictive performance.
Develop reproducible machine learning workflows and document technical methods and evaluation results.
Build Python-based analytical solutions using tools such as Pandas, scikit-learn, and scientific computing libraries.
Clean, transform, and explore data; perform feature engineering; and design data models, scripts, pipelines, and reusable workflows.
Review code for correctness, efficiency, maintainability, and consistency with analytical requirements.
Design and evaluate experiments using statistical testing, performance measurement, and outcome analysis.
Work with stakeholders to define experimental objectives, evaluation criteria, and analytical methods.
Review AI-generated solutions involving statistics, machine learning, experimentation, and quantitative reasoning; identify flawed methods, errors, incorrect assumptions, and weaknesses in reasoning.
Develop expert-level prompts, datasets, reference solutions, and structured feedback to improve AI model performance.
Prepare analytical reports describing methodologies, findings, statistical interpretations, and technical recommendations.
Document machine learning implementations, experiments, computational workflows, and evaluation results.
Review peer contributions for analytical accuracy, reproducibility, and methodological quality.
Communicate quantitative concepts effectively to technical and non-technical collaborators and support improvements to data science workflows, documentation, and AI evaluation standards.