Forecasting Data Scientist

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 help our business react to changing business conditions with excellent data analysis. You will define the requirements, perform the analysis and identify patterns in large amounts of data.

You will:

• Define requirements for analysis in a given business area and perform detailed analysis and identify trends defined in the requirements
• Identify patterns and help the business react to changing business conditions
• Perform root‑cause analysis and interpret data
• Work with large amounts of data such as facts, figures, and mathematics/formulas and undertake analytical activities and deliver analysis outputs in accordance with customer needs and conforming to established standards
• Understand and be involved with aspects of the data science process
• As the investigator on the data science team, you will apply your knowledge of languages and pull data out of SQL databases, using Tableau/Power BI type tools, and producing basic data visualizations and reporting dashboards

A desire to drive your future and accelerate your career and the following experience and knowledge:

• Knowledge of Microsoft Excel, Power BI, SQL, R, Python, SAS software, Google Analytics, Google Tag Manager, Tableau, Google AdWords, statistical software
• Moderate knowledge in math and statistical skills, strong business acumen, moderate computer science/coding skills
• Develop key performance indicators and create visualizations of the data
• Utilize business intelligence and analytics tools

You will represent and communicate data requirements to help us understand our data assets and the relationships among them. You will investigate, analyze and scope data requirements to support the development of data integration, data retrieval and reusable data sets.

The Data Scientist will be responsible for advanced forecasting methodologies for demand forecasting to generate better forecasting results in terms of accuracy and bias.

• Determine, create and maintain the best statistical models to be used, by considering SKU demand behavior using segmentation strategy, to generate high‑quality demand statistical forecast with low forecast error and bias
• Refine forecasting models, by reviewing forecast performance and incorporating feedback from the Demand Planner, to improve forecast error and bias metrics
• Analyse the model performance every month / week where MAPE (Main Absolute Percentage Error) is deteriorating and post‑process the output and if required finetune the output
• Propose additional strategies to have a better code (efficient or more accurate) to execute in Databricks
• Collaborate with Demand Planners to provide explainability of models and gather feedback to improve the models
• Lead, develop, and deploy some algorithms built on Databricks that help the predictive process to be more efficient and accurate

• Lead the statistical forecasting process to provide the business a statistical base forecast for its demand planning process
• Lead some continuous improvement projects to make models more efficient, robust, and accurate
• Analyse business requirements as a guide for data modelling and apply data analysis, design, modelling, and quality assurance techniques, based on a detailed understanding of business processes, to establish, modify or maintain data structures and associated components (entity descriptions, relationship descriptions, attribute definitions)
• Manage the iteration, review and maintenance of data requirements and data models and assist in creating the semantic layer of data
• Choose a suitable data modeling approach for each project, such as by assessing the suitability of existing data models and building data models with the flexibility to change when business requirements change
• Reconcile multiple logical source models into a single, logically consistent model and ensure that the proposed model follows data architecture guidelines and best practices
• Design, build, implement and/or maintain Python/PySpark algorithms to execute predictive analysis which help to improve planning processes
• Analyse code, define and implement strategies to have more accurate and efficient results

A desire to drive your future and accelerate your career and the following experience and knowledge:

• Extensive experience in data modelling in a large complex business with multiple systems
• Ability to simplify complex problems and communicate them to a broad audience
• Design, build, implement and/or maintain Python/PySpark algorithms to execute predictive analysis which help to improve planning processes
• Analyse code, define and implement strategies to have more accurate and efficient results
• Working in cloud computing environments with tools like Databricks and help MDLZ to have better practices on it
• Aligning and…