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 be crucial in supporting our business by creating valuable, actionable insights about the data, and communicating your findings to the business.  You will work with various stakeholders to determine how to use business data for business solutions/insights.

How you will contribute

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 modelingUnderstand 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/insightsEnable 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 resultsApply 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 dataDevelop processes, techniques, and tools to analyze and monitor model performance while ensuring data accuracyEvaluate 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 makingContribute 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

What you will bring

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

Strong quantitative skillset with experience in statistics and linear algebra.A natural inclination toward solving complex problemsKnowledge/experience with statistical programming languages including R, Python, SQL, etc., to process data and gain insights from itKnowledge of machine learning techniques including decision-tree learning, clustering, artificial neural networks, etc., and their pros and consKnowledge and experience in advanced statistical techniques and concepts including, regression, distribution properties, statistical testing, etc.Good communication skills to promote cross-team collaborationMultilingual coding knowledge/experience: Java, JavaScript, C, C++, etc.Experience/knowledge in statistics and data mining techniques including random forest, GLM/regression, social network analysis, text mining, etc. Ability to use data visualization tools to showcase data for stakeholders

More about this role

What you need to know about this position:

What extra ingredients you will bring:

Education / Certifications:

Job specific requirements:

Travel requirements:

Work schedule:

No Relocation support available
Business Unit Summary

Mondelēz International entered the China market in 1984. Headquartered in Shanghai, Mondelēz Greater China is a leading company in the snacks business, including biscuits, Chocolate, candy & gum, and beverages. With over 4,000 employees, Mondelēz has established manufacturing plants in East, South and North China as well as a Global Biscuit R&D Technical Center in Suzhou. The Chinese name Yi Zi (亿滋) represents the company’s vision to bring an abundance of deliciousness to consumers.

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.

Job Type

Regular

Data Science

Analytics & Data Science