Data Scientist – Personalization & Search

About the role

We are looking for a Mid/Senior Data Scientist to lead personalization and search initiatives within LC Waikiki’s Digital Transformation and IT Department. You will design and implement recommendation, ranking and retrieval systems that power product discovery across our e‑commerce and retail channels.

Key responsibilities

• Design and develop recommendation engines using collaborative, content‑based, hybrid and deep‑learning approaches.
• Build and optimise personalized ranking models for product recommendations, search result ordering and merchandising use cases.
• Implement dense, sparse and hybrid retrieval techniques, including semantic and vector‑based search.
• Develop end‑to‑end recommendation pipelines (candidate generation, retrieval, ranking, re‑ranking, diversification and business‑rule optimisation).
• Apply association‑rule mining and frequently‑bought‑together analysis to enhance cross‑sell and upsell.
• Create multimodal AI solutions that combine text, image and behavioural signals, leveraging models such as FashionCLIP, FashionSigLIP, CLIP and SigLIP.
• Build semantic similarity systems for visual search, outfit recommendation and catalog enrichment.
• Collaborate with business stakeholders, translate model outcomes into actionable recommendations and present results to senior leadership.
• Stay current with advances in recommendation, search and generative AI, prototype emerging techniques and contribute to innovation projects.

Required profile

• Proven experience as a Data Scientist or Machine Learning Engineer in personalization, search or recommendation domains.
• Strong ability to communicate complex technical concepts to both technical and non‑technical audiences.
• Track record of delivering production‑grade ML models in an Agile/Scrum environment.

Required skills

• Collaborative filtering, content‑based filtering and hybrid recommendation methods.
• Deep learning for retrieval and ranking architectures.
• Dense, sparse and hybrid retrieval, semantic search and vector search.
• Association rule mining and frequently‑bought‑together analysis.
• Multimodal embedding models (FashionCLIP, FashionSigLIP, CLIP, SigLIP).
• Agile/Scrum methodology.

What we offer

• Opportunity to shape AI‑driven personalization for a global fashion retailer.
• Work with a cross‑functional team of 850+ experts in a fast‑moving digital environment.
• Access to cutting‑edge AI research and the ability to prototype innovative solutions.