Quantitative Analyst ( Strong Python/Jupyter )
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.
· 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.