Data Scientist, Quantitative Analyst

This is a fully remote position, open to applicants in New York.

📋 Description

• Develop realistic analytical tasks that reflect professional data science and quantitative research methodologies.

• Create assignments that involve messy datasets, anomaly detection, correlation analysis, hypothesis testing, and method comparisons.

• Design intricate, multi-step problems that necessitate statistical judgment and careful interpretation.

• Ensure that tasks incorporate realistic constraints, datasets, assumptions, and decision-making objectives.

• Conduct reference analyses utilizing Jupyter Notebook or Google Colab.

• Construct clear and reproducible workflows using Python, along with libraries such as pandas and NumPy.

• Document decisions related to data cleaning, calculations, statistical methods, and analytical conclusions.

• Validate intermediate results, perform spot checks, create visualizations, and provide final recommendations.

• Design equitable comparisons between various analytical models, algorithms, or statistical methodologies.

• Assess performance using suitable metrics, manual checks, and sensitivity analyses.

• Identify methodological trade-offs, limitations, and sources of uncertainty.

• Generate recommendations that are substantiated by transparent quantitative evidence.

• Review model-generated analyses to ensure statistical accuracy, methodological rigor, and proper interpretation.

• Verify that calculations, correlations, hypotheses, and conclusions are adequately supported by the data.

• Detect coding errors, unsupported assumptions, misleading summaries, and analytical shortcuts.

• Clarify where and why model outputs fail to align with professional data-analysis standards.

• Collaborate closely with researchers, task authors, and fellow quantitative specialists.

• Compare evaluation decisions to uphold consistent benchmark standards.

• Refine tasks, reference notebooks, and grading criteria based on testing results.

• Document recurring model weaknesses and identify opportunities for enhanced evaluation coverage.

⛳️ Requirements

• A minimum of 1 year of experience in data science, quantitative analysis, research engineering, or a similar research-focused analytical position.

• Extensive hands-on experience with data cleaning, statistical correlation, hypothesis testing, and interpretation.

• Strong expertise in Python, particularly with pandas, NumPy, or other comparable analytical libraries.

• Experience utilizing Jupyter Notebook or Google Colab for analysis and reporting tasks.

• Proficient understanding of Git and reproducible analytical workflows.

• Capability to communicate complex quantitative findings clearly to both technical and non-technical stakeholders.

• Keen attention to detail and the confidence to navigate ambiguous, open-ended challenges.

• Reliable availability for approximately 35 hours per week.

• A master's degree or PhD in statistics, data science, mathematics, economics, computer science, engineering, or another quantitative field is highly relevant.

• Equivalent practical experience in a research-intensive analytical domain may also be considered.

• Academic or professional research experience involving statistical modeling, experimentation, or large-scale data analysis may enhance an application.

• Publications, technical reports, open-source contributions, or impactful analytical projects could also be advantageous.

🏝️ Benefits

• Opportunity to work on impactful projects within a collaborative environment.

• Professional development and growth opportunities.

• Flexible working hours and remote work options.