What are the responsibilities and job description for the Senior Data Analyst Public Cloud LRP Model Automation position at Jobs via Dice?
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Job Responsibilities:
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 normalization, 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:
Job Responsibilities:
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 normalization, 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.