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.
- 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