Finance Data Analyst

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.

Excel LRP / Driver-Based Model Experience Mandatory

• 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.

Source System Pipeline Engineering

• 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.

Model Versioning & Variance Analysis

• 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.

Qualifications

• 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.