

The Brixton Group
Senior Quantitative Analyst
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Quantitative Analyst with a contract length of 6+ months, 100% remote. Key skills include Python, Jupyter, SQL, Monte Carlo simulations, and cloud cost modeling (AWS, Azure, GCP). Experience in FP&A and data validation is required.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
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ποΈ - Date
April 7, 2026
π - Duration
More than 6 months
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ποΈ - Location
Remote
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π - Contract
Unknown
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π - Security
Unknown
-
π - Location detailed
San Jose, CA
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π§ - Skills detailed
#Plotly #Data Engineering #Forecasting #Matplotlib #Visualization #Pandas #Datasets #Storage #Azure #Version Control #dbt (data build tool) #Data Pipeline #NumPy #Airflow #"ETL (Extract #Transform #Load)" #AWS (Amazon Web Services) #Data Extraction #Cloud #Jupyter #Python #GCP (Google Cloud Platform) #SQL (Structured Query Language)
Role description
Duration: 6+ months
Location: 100% REMOTE
Requirements:
β’ Strong Python + Jupyter Notebook experience (heavy Pandas usage)
β’ Experience converting complex Excel models into Python (formula tracing, validation)
β’ Hands-on Monte Carlo simulation (P10/P50/P90, distributions, scenario modeling)
β’ Experience with cloud cost modeling (AWS, Azure, GCP - compute, storage, networking)
β’ Strong SQL for data extraction and analysis
β’ Experience building lightweight data pipelines (APIs, files, DB queries)
β’ FP&A-style forecasting, variance analysis, and driver-based modeling
β’ Experience with data validation, auditability, and versioning of model runs
β’ Ability to explain outputs and variance drivers to non-technical stakeholders
Key Responsibilities:
β’ Rebuild Excel-based cloud cost model into Python (Jupyter notebooks)
β’ Create automated data pipelines and clean Pandas datasets for modeling
β’ Build parameterized forecasting engine across cloud cost drivers
β’ Implement Monte Carlo simulations for probabilistic forecasting
β’ Develop variance analysis (actual vs forecast, forecast vs forecast)
β’ Deliver sensitivity analysis, scenario modeling, and driver ranking
β’ Build notebook-based visualizations (waterfalls, fan charts, etc.)
β’ Ensure full auditability and version control of model inputs/outputs
β’ Partner with FinOps, FP&A, Data Engineering, and Infrastructure teams
Nice to Have:
β’ Experience in FinOps, cloud economics, or cost modeling
β’ Familiarity with Airflow, Prefect, dbt, or scheduling tools
β’ Experience with Plotly, Matplotlib, or Bokeh
β’ Exposure to PyMC, NumPyro, or probabilistic modeling tools
26-00383
Duration: 6+ months
Location: 100% REMOTE
Requirements:
β’ Strong Python + Jupyter Notebook experience (heavy Pandas usage)
β’ Experience converting complex Excel models into Python (formula tracing, validation)
β’ Hands-on Monte Carlo simulation (P10/P50/P90, distributions, scenario modeling)
β’ Experience with cloud cost modeling (AWS, Azure, GCP - compute, storage, networking)
β’ Strong SQL for data extraction and analysis
β’ Experience building lightweight data pipelines (APIs, files, DB queries)
β’ FP&A-style forecasting, variance analysis, and driver-based modeling
β’ Experience with data validation, auditability, and versioning of model runs
β’ Ability to explain outputs and variance drivers to non-technical stakeholders
Key Responsibilities:
β’ Rebuild Excel-based cloud cost model into Python (Jupyter notebooks)
β’ Create automated data pipelines and clean Pandas datasets for modeling
β’ Build parameterized forecasting engine across cloud cost drivers
β’ Implement Monte Carlo simulations for probabilistic forecasting
β’ Develop variance analysis (actual vs forecast, forecast vs forecast)
β’ Deliver sensitivity analysis, scenario modeling, and driver ranking
β’ Build notebook-based visualizations (waterfalls, fan charts, etc.)
β’ Ensure full auditability and version control of model inputs/outputs
β’ Partner with FinOps, FP&A, Data Engineering, and Infrastructure teams
Nice to Have:
β’ Experience in FinOps, cloud economics, or cost modeling
β’ Familiarity with Airflow, Prefect, dbt, or scheduling tools
β’ Experience with Plotly, Matplotlib, or Bokeh
β’ Exposure to PyMC, NumPyro, or probabilistic modeling tools
26-00383






