Data Scientist в Mondelez

You will:
  • Analyze and derive value from data through the application methods such as mathematics, statistics, computer science, machine learning and data visualization. In this role you will also formulate hypotheses and test them using math, statistics, visualization and predictive modeling
  • Understand business challenges, create valuable actionable insights about the data, and communicate your findings to the business. After that you will work with stakeholders to determine how to use business data for business solutions/insights
  • Enable data-driven decision making by creating custom models or prototypes from trends or patterns discerned and by underscoring implications. Coordinate with other technical/functional teams to implement models and monitor results
  • Apply mathematical, statistical, predictive modelling or machine-learning techniques and with sensitivity to the limitations of the techniques. Select, acquire and integrate data for analysis. Develop data hypotheses and methods, train and evaluate analytics models, share insights and findings and continues to iterate with additional data
  • Develop processes, techniques, and tools to analyze and monitor model performance while ensuring data accuracy
  • Evaluate the need for analytics, assess the problems to be solved and what internal or external data sources to use or acquire. Specify and apply appropriate mathematical, statistical, predictive modelling or machine-learning techniques to analyze data, generate insights, create value and support decision making
  • Contribute to exploration and experimentation in data visualization and you will manage reviews of the benefits and value of analytics techniques and tools and recommend improvements

A desire to drive your future and accelerate your career and the following experience and knowledge:
  • Strong quantitative skillset with experience in statistical modelling, econometrics modelling such as Regression, Hierarchical Bayesian
  • Ability to use data visualization tools to showcase data for stakeholders
  • A natural inclination toward solving complex problems especially related to return on marketing investment, promo efficiency, model explianability and forecasting
  • Knowledge/experience with statistical programming languages including SAS, R, Python, SQL, etc., to process data and gain insights from it
  • Knowledge of different sales and sales promo data availability and their usage in 'driver analysis'
  • Knowledge and experience in advanced statistical techniques and concepts including, regression, distribution properties, statistical testing, etc.
  • Good communication skills to promote cross-team collaboration

The Data Scientist forecasting will be responsible for advanced modelling methodologies for forecasting explainability to generate better driver /promotion effectiveness analysis
  • Determine, create and maintain the best Statistical models be to be used that helps business explain impact of drivers on different demand metrices
  • Implement the best practice of promo efficiency model and lucidly explain that to business decision makers
  • Collaborate with Demand Planners to identify right drivers and lever which influences demand and thus incorporate in statistical forecasting process
  • Support SAS Implementation for market for demand modelling in SAS and SAS Model Improvement activity. Keep close liaison with SAS implementation partner to get transitioned process to Central Analytics Team
  • Refine forecasting models, by reviewing forecast performance and incorporating feedback from the Demand Planner, to improve forecast error and bias metrics
  • Analyze the model performance every month / week
  • Propose additional data elements which we can consume and work with ETL developer to get those into SAS staging and SAS ABTs
Either of the following is applicable as educational criteria for the position:
  • Degree/Masters in quantitative field of Statistics, Applied Mathematics or Engineering, with specific full-time courses in Analytics
  • Certifications any of SAS Base, SAS VF, SAS Visual Statistics, etc is a must
  • Strong Applied Knowledge of analytical techniques in statistical modelling, machine learning with exposure to forecasting domain especially driver based forecasting
  • Experience on working with FMCG, Food & Beverages, Retail or similar industry data with understanding the business process with be advantage
  • Should be able to articulate data science outcome into business understandable language
  • Fluent English, other European languages would be an advantage.


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