Senior Data Analyst

Senior Data Analyst at NLB Services Senior Data Analyst at NLB Services in Alviso, California Posted in about 22 hours ago.

Type:

full-time

Job Description:

Required Skills & Experience Jupyter Notebooks Financial Modelling - Mandatory Demonstrated, hands-on experience building production financial models directly in Jupyter Notebooks.

Portfolio / Git

Hub 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.