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Ahold Delhaize USA Logo

Data Scientist

Ahold Delhaize USA

$86,320 - $129,480
Oct 16, 2025
Chicago, IL, US
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Ahold Delhaize USA is seeking to develop industry-leading machine learning / AI algorithms to enhance personalization and recommender engines for its omnichannel grocery brands, including Food Lion, Giant Food, The GIANT Company, Hannaford, and Stop & Shop.

Requirements

  • 3+ years of applied Data Science experience
  • 2+ years of experience deploying, scaling, and continually improving algorithms
  • Broad industry experience with ML/AI/GenAI and commonly used statistical methods
  • Expert recommender systems and personalization algorithms
  • Expert hands-on knowledge of current AI/GenAI solutions
  • Fluent knowledge and experience with using LLMs
  • Above 95th percentile coding ability in Python and PySpark

Responsibilities

  • Create algorithms to better personalize every customer touch point
  • Develop our core algorithms with strong methodological rigor
  • Identify strengths, weaknesses, and potential biases of modeling components
  • Consult with our brands and business partners to create ML/AI solutions to their largest business challenges
  • Assist Machine Learning Engineers in the development of SDKs for rapid prototyping and deployment of algorithms
  • Build robust experimentation algorithms to continually improve existing algorithms

Other

  • Bachelor's degree in Statistics, Mathematics, Economics; Master's Degree preferred
  • Applicants must be currently authorized to work in the United States on a full-time basis
  • Flexible/hybrid work schedule includes 3 in-person days at one of our core locations and 2 remote days
  • Deep understanding of, and experience applying, the following ML concepts: Collaborative filtering, Linear and scalar algebra, Association rule mining, Ranking algorithms, Advanced regression, classification, clustering, and time series algorithms, Automated hyperparameter tuning and model selection, Causal inference modeling at scale for algorithm testing and evaluation, Deep learning at scale for NLP