Remote | Data Scientist & Quantitative Analyst

About the role

We are seeking skilled data scientists and quantitative analysts for a full-time consulting position. This role focuses on statistical analysis, data preparation, method evaluation, reproducible research, and data-driven reporting. You will contribute to the creation of advanced evaluation benchmarks for AI models.

Key facts

What you'll do

• Design realistic data analysis tasks that reflect authentic data science workflows.
• Develop assignments that involve complex data sets, including tasks related to anomaly detection and hypothesis testing.
• Create reproducible reference notebooks using tools like Jupyter Notebook or Google Colab, employing Python and relevant libraries.
• Conduct statistical method comparisons, evaluating different models and algorithms for performance and reliability.
• Review and assess AI-generated analyses for accuracy and adherence to statistical standards.
• Collaborate with researchers and other quantitative professionals to refine benchmarks and evaluation criteria.

Requirements

• Minimum of 1 year of experience in data science, quantitative analysis, or a related analytical role.
• Proficient in data cleaning, statistical correlation, hypothesis testing, and interpretation.
• Strong skills in Python, particularly with libraries such as pandas and NumPy.
• Familiarity with Jupyter Notebook or Google Colab for data analysis and reporting.
• Understanding of Git and reproducible analytical practices.
• Ability to convey complex quantitative insights to both technical and non-technical audiences.
• Attention to detail and comfort in navigating ambiguous problems.
• Availability for approximately 35 hours per week.

Educational Background

• A master's degree or PhD in a quantitative field such as statistics, data science, mathematics, economics, computer science, or engineering is preferred.
• Equivalent practical experience in a research-focused analytical area will also be considered.
• Experience in statistical modeling, experimentation, or large-scale data analysis is advantageous.
• Contributions to publications, technical reports, or significant analytical projects will be viewed favorably.

Nice to have

• Experience in AI model training, evaluation, or benchmark development.
• Background in creating analytical tasks, reference solutions, or grading criteria.
• Knowledge of anomaly detection, experimental design, or comparative model evaluation.
• Experience in validating automated analyses through manual checks.
• Familiarity with statistical modeling, machine learning, or scientific computing.
• Understanding of agentic AI systems and multi-step model evaluations.
• Experience reviewing analyses or notebooks created by peers.
• Strong capability to identify subtle statistical inaccuracies and unsupported conclusions.

Why This Opportunity

• Utilize your expertise in data science and quantitative analysis to enhance AI evaluation methods.
• Create realistic analytical tasks that reflect professional standards.
• Contribute to improving AI systems' understanding of statistics and data quality.
• Engage with Python, reproducible notebooks, model evaluation, and more.