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Senior Quantitative Analyst

Roles & Responsibilities

  • Strong Python and Jupyter notebook experience with heavy Pandas usage for joining, reshaping, aggregating, and validating multiple data frames; familiarity with NumPy and SciPy statistics.
  • Hands-on experience building Monte Carlo simulations including distribution selection and fitting, sampling design, and interpreting P10 P50 P90 results; experience handling correlated variables is a plus.
  • Experience converting complex Excel models into code by tracing formulas, documenting assumptions, and validating numeric accuracy.
  • Ability to build reliable Python data pipelines (API authentication, pagination, schema normalization, error handling, incremental refresh) and strong SQL skills for extracting inputs from data warehouses or billing databases; exposure to versioning and auditability beyond Git.

Requirements:

  • Develop and validate Monte Carlo simulations, including distribution selection/fitting, sampling design, interpreting P10/P50/P90 results, and communicating findings.
  • Build and maintain reliable Python data pipelines with emphasis on API integration, pagination, schema normalization, error handling, and incremental refresh; ensure data quality and reproducibility.
  • Translate complex Excel models into code by tracing formulas, documenting assumptions, and validating numeric accuracy.
  • Explain model outputs and variance drivers to non-technical stakeholders and produce business-ready commentary.

Job description

Required skills and experience

· Strong Python and Jupyter notebook experience with heavy use of Pandas for joining, reshaping, aggregating, and validating multiple data frames. Experience with NumPy and SciPy statistics is highly preferred.

· Hands on experience building Monte Carlo simulations including distribution selection and fitting, sampling design, and interpreting P10 P50 P90 results. Experience handling correlated variables is a plus.

· Experience converting complex Excel models into code by tracing formulas, documenting assumptions, and validating numeric accuracy.

· Ability to build reliable data pipelines in Python including API authentication, pagination, schema normalization, error handling, and incremental refresh.

· Strong SQL skills for extracting inputs from data warehouses or billing databases.

· Experience with versioning and auditability beyond Git, including structured snapshot storage of inputs and outputs.

· Comfort explaining model outputs and variance drivers to non-technical stakeholders and producing business ready commentary.

Nice to have

· Prior work in FP&A, cloud economics, FinOps, or cost and usage modeling for AWS, Azure, or GCP.

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