Senior Quantitative Analyst

Role summary 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.