Senior Quantitative Analyst (689)
Artech LLC
Irving, TX
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JOB DETAILS
SKILLS
Analysis Skills, Billing, Business Model, Cadence, Capacity Management, Cloud Computing, Cost Modeling, Data Sets, Debugging Skills, Financial Planning and Analysis (FP&A), Forecasting, Monte Carlo Method, Python Programming/Scripting Language, Quantitative Analysis, Simulation
LOCATION
Irving, TX
POSTED
1 day ago
Location US Remote
Duration 3-6 Months
Job Description:
We are seeking a senior quantitative analyst with strong hands on Python skills to help build and maintain forecasting and simulation models for long range planning. This role is focused on translating business logic and assumptions into working Python code, performing data analysis using Pandas, and implementing Monte Carlo simulations to quantify uncertainty. Cloud usage and billing data may be used as input data, but deep FinOps or cloud cost expertise is not required.
This is a highly hands on individual contributor role. The primary expectation is strong Python coding ability.
What you will do
Write clean, correct Python code to analyze time series and tabular datasets using Pandas and NumPy.
Implement Monte Carlo simulations to model uncertainty in forecasts and produce confidence intervals (e.g., P10, P50, P90).
Convert existing Excel based models or business logic into maintainable Python implementations.
Aggregate, clean, and transform real world datasets (CSV or table based) to produce forecasting inputs and outputs.
Build notebook based analysis that can be re run regularly with updated data.
Add basic sensitivity analysis to understand which inputs most affect forecast outcomes.
Work with stakeholders to clarify assumptions and validate results, focusing on correctness, transparency, and reproducibility.
Required skills and experience
Strong Python proficiency, including writing functions from scratch and debugging code live.
Strong Pandas experience, including:
groupby and aggregation
joins and merges
time based grouping and sorting
handling missing or messy data
Hands on experience implementing Monte Carlo simulations in Python, not just describing them conceptually.
Familiarity with NumPy random sampling and percentile calculations.
Experience translating Excel models or analytical logic into Python with validation of results.
Comfortable working in Jupyter notebooks.
Nice to have (not required)
Experience working with cloud usage or billing datasets.
Exposure to forecasting, FP&A, capacity planning, or cost modeling.
Experience building models that are refreshed on a regular cadence (weekly or monthly).
Familiarity with basic model versioning or reproducibility patterns.
What success looks like
Can take a dataset and produce correct summaries and forecasts without hand holding.
Can implement Monte Carlo simulations that run, produce correct distributions, and are explainable.
Writes Python that others can read, rerun, and validate.
Handles live coding and problem solving without getting overwhelmed by the environment.
About the Company
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