Job Description
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
26-00317
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