Applied Scientist

About the work

Evaluate AI quantitative reasoning while building statistical analyses, machine learning models, and reproducible Python workflows. This remote contract values practical experimentation and data science experience; prior AI experience is not required.

Responsibilities

• Design and execute statistical analyses to uncover actionable insights from complex datasets.
• Develop and validate machine learning models for various business or research objectives.
• Apply data modeling techniques and manage large-scale data transformations using Python and Pandas.
• Collaborate with stakeholders to define experimentation frameworks and analyze outcomes.
• Document methodology, findings, and recommendations with a focus on clarity and precision.
• Review peer contributions and provide high-quality feedback to support knowledge sharing.
• Deliver reproducible code and well-organized analytical reports as part of the project deliverables.
• Evaluate AI model outputs on statistics, machine learning, experimentation, and quantitative problems.
• Create expert-level prompts, datasets, and reference solutions.
• Identify flawed methodology, statistical errors, and weak quantitative reasoning.
• Provide clear, structured feedback that improves model capability.

Preferred background

• At least one year at a top company (tech, finance, or research).
• Expertise in Python, including proficiency with libraries such as Pandas and scikit-learn (sklearn).
• Demonstrated experience in applied statistics, experimental design, and machine learning.
• Proven ability to develop, tune, and evaluate machine learning models in practical settings.
• Strong data modeling skills and experience managing structured and unstructured data.
• Excellent written and verbal communication skills, with a passion for clear technical documentation.
• Experience collaborating on open or cross-functional data science projects is a plus.
• Advanced academic background or hands-on work in data science, applied mathematics, or related fields.