Senior Quantitative Analyst, Cloud Cost Forecasting & Modeling

Duration: 6+ months

Location: 100% REMOTE

Requirements:

• Strong Python + Jupyter Notebook experience (heavy Pandas usage)
• Experience converting complex Excel models into Python (formula tracing, validation)
• Hands-on Monte Carlo simulation (P10/P50/P90, distributions, scenario modeling)
• Experience with cloud cost modeling (AWS, Azure, GCP - compute, storage, networking)
• Strong SQL for data extraction and analysis
• Experience building lightweight data pipelines (APIs, files, DB queries)
• FP&A-style forecasting, variance analysis, and driver-based modeling
• Experience with data validation, auditability, and versioning of model runs
• Ability to explain outputs and variance drivers to non-technical stakeholders

Key Responsibilities:

• Rebuild Excel-based cloud cost model into Python (Jupyter notebooks)
• Create automated data pipelines and clean Pandas datasets for modeling
• Build parameterized forecasting engine across cloud cost drivers
• Implement Monte Carlo simulations for probabilistic forecasting
• Develop variance analysis (actual vs forecast, forecast vs forecast)
• Deliver sensitivity analysis, scenario modeling, and driver ranking
• Build notebook-based visualizations (waterfalls, fan charts, etc.)
• Ensure full auditability and version control of model inputs/outputs
• Partner with FinOps, FP&A, Data Engineering, and Infrastructure teams

Nice to Have:

• Experience in FinOps, cloud economics, or cost modeling
• Familiarity with Airflow, Prefect, dbt, or scheduling tools
• Experience with Plotly, Matplotlib, or Bokeh
• Exposure to PyMC, NumPyro, or probabilistic modeling tools

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