🌎Applied Scientist, Remote- Contract
About The Position
The Applied Scientist will help train next-generation AI systems by providing high-quality, real-world input that shapes how models learn, reason, and perform. The work is part of an open Data Scientist project and emphasizes clear written and verbal communication. Prior AI experience is not required; domain knowledge is valued. The role includes designing and executing statistical analyses, developing and validating machine learning models, applying data modeling techniques, and managing large-scale data transformations using Python and Pandas. The scientist will collaborate with stakeholders on experimentation frameworks, analyze outcomes, document findings, review peer contributions, and deliver reproducible code and organized analytical reports. The work also involves evaluating AI model outputs on quantitative topics, creating expert-level prompts, datasets, and reference solutions, identifying flawed methods or reasoning, and providing structured feedback to improve model capability.
Requirements
- Python skills.
- Machine learning skills.
- Statistics skills.
- Data analysis skills.
- Experimentation skills.
- Pandas skills.
- Data modeling skills.
- Excellent written and verbal communication skills.
- Passion for clear technical documentation.
Nice To Haves
- At least one year at a top company in technology, finance, or research.
- Proficiency with Python libraries such as Pandas and scikit-learn (sklearn).
- Demonstrated experience in applied statistics, experimental design, and machine learning.
- Experience developing, tuning, and evaluating machine learning models in practical settings.
- Experience managing structured and unstructured data.
- Experience collaborating on open or cross-functional data science projects.
- Advanced academic background or hands-on work in data science, applied mathematics, or related fields.
Responsibilities
- Design and execute statistical analyses to uncover actionable insights from complex datasets.
- Develop and validate machine learning models for 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 clearly and precisely.
- Review peer contributions and provide high-quality feedback to support knowledge sharing.
- Deliver reproducible code and well-organized analytical reports as 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.
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What This Job Offers
Education Level
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