Company Profile:

Central Analytics Team at Mondelez International provides visibility, insights and solutions across the globe to different supply chain pillar teams in order to help the organization make better decisions. This is a rapidly growing and fun team with talented people of diverse experiences and cultures. If you have the passion to solve complex business problems and create an impact, then we have an opportunity for you.

Job description:

CAT Team

Central Analytics Team at Mondelez International provides visibility, insights and solutions across the globe to different supply chain pillar teams in order to help the organization make better decisions. This is a rapidly growing and fun team with talented people of diverse experiences and cultures. If you have the passion to solve complex business problems and create an impact, then we have an opportunity for you. Please apply through this link.

Job Profile:

Data Scientist – Forecasting/ Sr. Data Scientist – Forecasting/ DataBricks Platform Owner.

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)