Python Data Analyst for Financial Time-Series Modeling

I am seeking an experienced data analyst to support a financial data project involving large, multi-source datasets. The task will require cleaning, restructuring, and transforming raw financial time-series data using Python (Pandas, NumPy) or R (tidyverse, data.table). The ideal candidate should be comfortable handling panel data, missing values, outlier treatment, and variable engineering to prepare the dataset for advanced econometric modeling. Following data preparation, the project will involve implementing a Vector Autoregression (VAR) or panel-based econometric model in R (vars, plm) or Python (statsmodels). You will be expected to conduct diagnostic testing (stationarity, cointegration, multicollinearity), interpret impulse response functions, and generate forecast scenarios where applicable. Experience working with macroeconomic or financial indicators is highly desirable. The final deliverables should include well-documented code, reproducible analysis, and a concise technical report explaining methodology, assumptions, model validation, and key findings. Clear data visualization and interpretation of results for non-technical stakeholders will also be required. Strong attention to methodological rigor and communication skills.