Job description
Job Description
Are You Ready to Make It Happen at Mondelēz International?
Join our Mission to Lead the Future of Snacking. Make It With Pride.
You will part of the Mondelēz Supply Chain Central Analytics Team (MSC CAT) which is a global supply chain analytics organization and is responsible for all advanced statistical forecasting needs for the AMEA (Asia, Middle East, Africa) Region.
The main objective of the role is to improve Sales Forecast Accuracy and drive adoption of holistic forecasting.
What you will Bring
- A desire to drive your future and accelerate your career and the following experience and knowledge:
- Alteast 5-8 years of work experience in building AI/ML based solutions
- B.E/B.Tech/M.Tech/B.Sc in. Computer Science, Engineering, Statistics, Operations Research); preferably a postgraduate (Masters or Doctorate) degree
- Expertise in the areas of Machine Learning, Statistical Modelling, Explainable AI/ML(SHAP, LIME) and forecasting expertise to drive value to Demand Planning process
- Candidate should be well verse in all recent forecasting techniques like Random Forest, XBG, FBProphet, Greykite, MLR, ARIMAX, LSTM, etc. including various AutoML on different tools.
- Deep understanding of Databricks or PySpark , running Python on Spark through PySpark
- To promote cross-team collaboration: Sharing and enforcing ‘good practices’ in Predictive Analytics using AI & ML
- Problem resolution mindset: A natural inclination toward solving forecasting problems such as root cause analysis on lower forecast accuracy
- Drive continuous improvement in Forecasting Solution Implementation for demand modelling in DataBricks & Spark and Model Forecast Improvement activity.
What you need to know about this position:
The Data Scientist forecasting will be responsible for implementing and sustaining advanced forecasting methodologies for demand forecasting to generate better forecasting results in terms of accuracy and bias
- Development & Deployment of state of art ML/AI models for demand forecasting in Databricks with Apache PySpark .
- Determine, create, and maintain the best Statistical models be to be used, by considering SKU demand behaviour using segmentation strategy, to generate high quality demand statistical forecast with low forecast error and bias
- Implementation of scalable data science solution with market explainability features
- Manage forecast run performance to achieve forecast accuracy, bias and no touch adoption targets
- Collaborate with Demand Planners to identify right drivers and lever which influences demand and thus incorporate in statistical forecasting process
- Derive continuous improvement opportunities (incorporate them in test env. trial and move to production in consultation with BU, System Admin)
Country to country Relocation support available through our Global Mobility Policies
Business Unit Summary
At Mondelēz International, our purpose is to empower people to snack right by offering the right snack, for the right moment, made the right way. That means delivering a broad range of delicious, high-quality snacks that nourish life's moments, made with sustainable ingredients and packaging that consumers can feel good about.
We have a rich portfolio of strong brands globally and locally including many household names such as Oreo , belVita and LU biscuits; Cadbury Dairy Milk , Milka and Toblerone chocolate; Sour Patch Kids candy and Trident gum. We are proud to hold the top position globally in biscuits, chocolate and candy and the second top position in gum.
Our 80,000 makers and bakers are located in more than 80 countries and we sell our products in over 150 countries around the world. Our people are energized for growth and critical to us living our purpose and values. We are a diverse community that can make things happen—and happen fast.
Mondelēz International is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation or preference, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
