Applied Scientist

Requirements:
3-6 years of experience working on personalisation, recommendations, search, or ranking at scale.
Prior experience in a B2C product - social, e-commerce, fashion, dating, gaming, or video platforms.
Exposure to a wide range of popular recommendation and personalisation techniques, including collaborative filtering, deep retrieval models (e. g., two-tower), learning-to-rank, embeddings with ANN search, and LLM approaches for sparse data personalisation.
Exposure to training models OR deploying them - experience with end-to-end ML pipelines.
Understand offline and online evaluation, A/B testing, and metric alignment.
Experience with vector search, graph-based algorithms and LLM-based approaches is a big plus.

Experience
3-6 yrs