Data Science Analyst

AB InBev

Leuven, Belgium

The Purpose (of the role):

As a Consumer Data Scientist you will join our Commercial Analytics Data Science team and help us build capabilities that will propel ABInBev Zone Europe to the top of FMCG landscape in terms of the analytical maturity. We are still a relatively small team, but with an irrational ambition to: embed the test-and-learn paradigm into our company’s decision making processes like Amazon; provide relevant recommendations to our clients like Netflix; and understand our end-consumers like Facebook. We offer a unique opportunity to join us in the middle of our journey and to work on challenging problems within a truly meritocratic culture where excellence is fully recognized and generously compensated. This role will support both BEES and Ta Da digital platforms.

The role:

  • PREDICTION MODELLING & MEASUREMENT – Working with our Global Analytics Centers to further update, scale and deploy these capabilities in support to increase commercial KPIs;
  • MODEL MAINTENANCE – working to maintain and update data pipelines, model health and performance across multiple models across multiple markets;
  • LIVE TESTING - Extend the capabilities of our test-and-lean platform and support our internal clients in designing and executing experiments in the market;
  • ONLINE PROMOTION ENGINE – Optimize the promotion calendar, execution mechanics and the level of personalization to increase conversion and enhance customer lifetime value;
  • Work with the rest of the Data Science team on constant improvement of tools and methodology.

Who we’re looking for:

  • Academic degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, Economics…). Candidates with other backgrounds will be considered too provided that they can demonstrate relevant quantitative skills and experience;
  • At least 3 years of relevant working experience as a Data Scientist or in a closely related role;
  • Good programming skills in Python and/or R, including common Data Science toolkits & packages;
  • Excellent command of techniques used in each stage of Data Science workflow: ETL, EDA, feature engineering, modelling, evaluation of learning algorithms;
  • Profound understanding of Machine Learning algorithms for classification & regression;
  • Good understanding of Descriptive and Inferential Statistics;
  • Excellent Communication skills and good knowledge of Data Visualization concepts and techniques;
  • English language proficiency.

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