Job Description

The Pluggable Science team (Customer Engagement & media) at dunnhumby works towards testing and deployment of various Product Science solutions created via R&D. The team works with specialist Product and Engineering teams to aid market-level product deployments, including R&D that might be needed for customization of existing science to aid easier deployment.

The team’s work profile includes creation of personalization algorithms and running A/B Testing for some of the biggest retailers and CPG companies. These algorithms are used in real-time recommender systems embedded on client websites/apps through dunnhumby products.

Preferred Qualifications

• Bachelor’s degree or higher in a quantitative/technical field (e.g. Computer Science, Statistics, Engineering)

• 3+ years of relevant experience Machine Learning/ Statistical Algorithms/ Predictive Modelling – Boosting techniques, Decision Trees, Random Forests, Logistic Regression, Neural Nets, SVM, Clustering Techniques (k-means, DBSCAN, Affinity Propagation, etc), Optimization Techniques – Non Linear Programming, Genetic Algorithm, Gradient Boosting, etc.

• Hands-on experience in scripting languages like Python, Apache Spark, Scala, etc. Experience in in Data Structures, Big data handling would be preferred

• Sharp analytical abilities, proven design skills, excellent communication skills

• Experience with software coding practices is a strong plus

• Experience using Linux/UNIX to process large data sets