Senior Data Analyst

Required Skills & Experience • 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. Required Skills & Experience 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.