Lead Data Scientist with Prophet, Python

Responsibilities Build "should-cost" models through time-series and econometric methods Integrate macroeconomic factors and economic indicators as cost drivers Model relationships linking commodity prices, spend data, and external economic variables Ensure model outputs are interpretable to stakeholders by highlighting the factors behind each cost estimate Use time-series cross-validation techniques to guarantee model reliability Guard against data leakage and overfitting within time-series models Differentiates between correlation and cointegration, identifying when each is applicable Choose appropriate features for econometric and time-series models Utilize spend data and commodity price indexing to enhance procurement analytics Requirements 5+ years in data science, specializing in econometric and time-series modeling Skilled in Python, Prophet, and Chronos Background in procurement analytics, including spend data and commodity price indexing Knowledge of economic indicators, spend analysis, commodity pricing, and supplier diversity metrics Strong command of time-series cross-validation and explainable AI Demonstrated skill in preventing data leakage and overfitting in time-series models Clear grasp of the distinction between correlation and cointegration, and their respective use cases B2-level English proficiency or above #J-18808-Ljbffr EPAM Systems