Forecasting Analyst - Functional Analytics

Are You Ready to Make It Happen at Mondelez International?

Join our mission to lead the future of snacking. Make it with pride.

Together with analytics team leaders you will support our business with excellent data models to uncover trends that can drive long-term business results.

How you will contribute:

• Execute the business analytics agenda in conjunction with analytics team leaders
• Work with best-in-class external partners who leverage analytics tools and processes
• Use models/algorithms to uncover signals/patterns and trends to drive long-term business performance
• Execute the business analytics agenda using a methodical approach that conveys to stakeholders what business analytics will deliver

What you will bring:

• Using data analysis to make recommendations to analytic leaders
• Understanding in best-in-class analytics practices
• Knowledge of indicators (KPI's) and scorecards
• Knowledge of BI tools like Tableau, Excel, Alteryx, R, Python, etc. is a plus

More about this role:

We are seeking a skilled and motivated Data Scientist to support the Cocoa Enterprise Program - a strategic initiative focused on cocoa supply chain strategy, forecasting, and scenario planning. The successful candidate will play a pivotal role in developing, maintaining, and enhancing forecasting models that inform cocoa sourcing strategy, pricing decisions, and supply chain planning across the enterprise.

This role operates within Mondelez's internal data science ecosystem (Databricks, cloud platforms). Beyond technical expertise, the ability to translate complex model outputs into actionable strategy recommendations will be key to success.

What you need to know about this position:

• Forecasting Model Development: Design, develop, and operationalize advanced forecasting models for cocoa market dynamics including supply availability, demand planning, and price forecasting. This product serves as an input into Mondelez' scenario planning and optimization.
• Platform Compliance & Model Deployment: Ensure all model integration and deployment activities comply with Mondelez data science standards, including adherence to approved cloud architectures, MLOps practices, security protocols, and governance frameworks.
• Cross-Functional Collaboration: Partner with procurement, supply chain planning, finance, and commodity trading teams to align model outputs with business decision-making.
• Model Monitoring & Governance: Implement model monitoring frameworks for early detection of forecast drift and performance degradation; maintain comprehensive documentation of models, logic, and performance metrics.
• GenAI & Advanced Analytics: Explore and apply GenAI capabilities (e.g., LLMs for market intelligence summarization, information retrieval from unstructured commodity reports) to enhance the program's analytical capabilities.

What extra ingredients you will bring:

• Experience in agriculture, mining, manufacturing and or commodity markets (cocoa, coffee, sugar, or agricultural commodities)
• Exposure to GenAI tools and techniques - LLMs, RAG, Transformers - particularly for unstructured data processing (market reports, news, research papers)
• CPG industry experience, particularly in raw material sourcing or supply chain strategy

Education / Certifications:

• Education: Bachelor's degree in a quantitative discipline (Economics, Statistics, Mathematics, Operations Research, or STEM field). Master's preferred.
• Experience: Minimum 3 years of data science/AI experience, with a strong focus on forecasting and/or supply chain analytics.
• Domain Knowledge: Minimum 3 years of experience in supply chain, agriculture, mining, or other related field.

Job specific requirements:

• Forecasting & Time Series: Experience with time series modeling (ARIMA, Prophet, exponential smoothing, state-space models), driver-based and causal modeling approaches (structural equation models, causal inference, Bayesian structural time series), ML-based forecasting (gradient boosting, neural networks), and deep learning approaches (LSTMs, Transformers).
• Explainable AI: Experience with model interpretability and explainability techniques (SHAP, LIME, partial dependence plots, counterfactual explanations) to ensure model outputs are transparent and actionable for business stakeholders, particularly where models feed into scenario planning.
• Programming: Python, PySpark, SQL.
• MLOps: Experience with model productionization, CI/CD pipelines, model monitoring, and lifecycle management (MLflow or equivalent).
• Statistical Foundations: Solid background in statistics, probability distributions, hypothesis testing, A/B testing, and multivariate analysis.
• Communication: Ability to articulate complex model outputs and recommendations to leaders and non-technical stakeholders.

This administrative vacancy may be eligible for the hybrid model, allowing remote work on certain days of the week.

No relocation support available.