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Key Responsibilities
Jupyter Notebooks Financial Modelling Mandatory
Key Responsibilities
Jupyter Notebooks Financial Modelling Mandatory
- Demonstrated, hands-on experience building production financial models directly in Jupyter Notebooks. Portfolio / GitHub links required analysis-only notebooks do not qualify.
- Proven track record implementing Monte Carlo simulations in Python: distribution fitting, correlated variable sampling, simulation loop design, and P10/P50/P90 output interpretation.
- Proficiency in core Python modelling stack: NumPy, Pandas, SciPy (stats), and at least one Monte Carlo framework (PyMC, NumPyro, custom simulation engine, or equivalent).
- Experience with Plotly, Matplotlib, or Bokeh for financial chart types: waterfall bridges, fan charts, tornado charts.
- Direct experience working with or converting complex Excel-based LRP, 3-statement, or driver-based financial models (multi-tab, formula-intensive, 3 5 year horizon).
- Ability to reverse-engineer Excel model logic tracing precedents, documenting assumptions, and translating formula chains into equivalent Python with verified numerical accuracy.
- Understanding of driver-based modelling methodology: separating volume drivers from price/rate drivers, building assumption sensitivity tables, structuring base/upside/downside scenarios.
- Ability to build lightweight ETL/ELT pipelines in Python: API authentication, pagination, schema normalisation, error handling, and incremental refresh logic.
- SQL proficiency for data extraction from billing databases, data warehouses
- Experience with data pipeline orchestration tools (Airflow, Prefect, dbt, or notebook scheduling) is advantageous.
- Experience designing model version control beyond Git structured snapshot storage of inputs, assumptions, and outputs to enable point-in-time model reconstruction.
- Familiarity with variance bridge / waterfall decomposition methodologies used in FP&A (price-volume-mix, driver attribution).
- Comfort building automated commentary or structured output that explains numerical movements in business terms.
- Bachelor's degree in Finance, Economics, Mathematics, Computer Science, or a quantitative discipline. CFA, CIMA, CPA, or AFP/FP&A certification is a plus.
- 5 years in financial modelling, FP&A, or cloud economics roles.
- 3 years of hands-on Python / Jupyter financial modelling (not data science alone).
- Demonstrable Git proficiency; experience with code review in a team modelling context is preferred.