Data scientist in procurement analytics

We're looking for a Lead Data Scientist to create data-driven «should-cost» models that predict fair pricing for goods and services, using time-series and econometric approaches to power procurement analytics.

• 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
• Differentiate 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

• 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

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