Data Scientist - Personalization & Search

r:Our projects touch not only the retail world but also every corner of the digital realm. If you're excited to be part of this journey, here are the opportunities awaiting yo

u: Personalization, Search and Recommendation Syst

ems* Design and develop recommendation systems using collaborative filtering, content-based filtering, hybrid recommendation approaches, and deep learning-based retrieval architectur

es,* Build and optimize personalized ranking models for product recommendations, search result ranking, and merchandising use cas

es,* Develop retrieval systems leveraging dense, sparse, and hybrid retrieval techniqu

es,* Implement and improve search relevance using semantic search, vector search, and multimodal retrieval approach

es,* Design recommendation pipelines including candidate generation, retrieval, ranking, re-ranking, diversification, and business-rule optimization stag

es,* Apply association rule mining and frequently-bought-together techniques to discover product affinities and improve cross-sell and upsell experienc

es. Fashion AI and Multimodal Intellig

ence* Develop machine learning solutions utilizing text, image, and behavioral signals to improve personalization and product discovery experien

ces,* Work with multimodal embedding models such as FashionCLIP, FashionSigLIP, CLIP, SigLIP, and similar architectures for fashion understanding ta

sks,* Build semantic similarity systems for product matching, visual search, outfit recommendation, and catalog enrichm

ent,* Explore emerging multimodal AI techniques to enhance customer engagement and product discoverabil

ity. Stakeholder Manag

ement* Act as a key partner for business stakeholders on personalization and search initiat

ives,* Translate complex machine learning concepts into actionable business recommendat

ions,* Communicate model performance, experimentation results, and business impact to technical and non-technical audie

nces,* Present findings and recommendations to senior leadership and product t

eams. Innovation and Re

search* Stay up to date with advancements in recommendation systems, search technologies, deep learning, and Generati

ve AI,* Evaluate and prototype emerging methodologies, architectures, and tools to improve personalization capabil

ities,* Contribute to innovation initiatives by identifying and testing new opportunities in AI-driven customer experi

ng For?We're looking for a passionate and innovative Mid/Senior Data Scientist who enjoys building intelligent personalization, recommendation, and search systems that directly impact customer exper, ications* Minimum of 2 years of experience in Data Science, Machine Learning, Personalization, Recommendation Systems, Search, or related

domains,* Strong proficiency in Python and modern machine learning development pr

actices,* Strong understanding of supervised learning algorithms, particularly tree-based methods such as LightGBM, XGBoost, CatBoost, and Random

Forests,* Hands-on experience designing, training, tuning, and deploying deep neural network architectures, including retrieval and ranking models for personalization us

e cases,* Experience with TensorFlow, TensorFlow Recommenders (TFRS), PyTorch, or similar deep learning fra

meworks,* Comprehensive understanding of recommendation system methodologies in

• cluding:Collaborative F
• ilteringContent-Based F
• ilteringHybrid Recommendation
• SystemsMatrix Facto
• rizationTwo-Tower Retrieva
• l ModelsLearning-to-Rank Ap

proaches* Experience with product affinity and market basket analysis techniques in

• cluding
• :AprioriF
• P-GrowthAssociation Rul
• e MiningFrequently Bought Together

Systems* Strong understanding of retrieval architectures in

• cluding:Dense R
• etrievalSparse R
• etrievalHybrid R
• etrievalSemanti
• c SearchVecto

r Search* Good understanding of embedding technologies and multimodal representation learning, particularly within the fashion

domain,* Experience with multimodal embedding models such as FashionCLIP, FashionSigLIP, CLIP, SigLIP, or equivalent archit

ectures,* Experience working with vector databases and ANN search technologies such as FAISS, Milvus, Vertex AI Vector Search, Azure AI Search, Pinecone, Weaviate, or similar pl

atforms,* Proficiency in cloud environments such as Google Cloud Platform and Microsof

t Azure,* Experience deploying machine learning and recommendation solutions through batch and real-time serving archit

ectures,* Understanding of MLOps concepts including model versioning, monitoring, experimentation tracking, and CI/CD wo

rkflows,* Strong SQL skills and experience working with large-scale d

atasets,* Excellent analytical, problem-solving, and communication

skills. Preferred Quali

fications* Experience building large-scale recommendation systems in retail, e-commerce, marketplaces, media, or consumer-facing

products,* Experience with ranking models such as LightGBM Ranker, XGBoost Ranker, LambdaMART, or neural ranking archi

tectures,* Experience with A/B testing frameworks and online experimentation metho

dologies,* Experience with feature stores, real-time feature engineering, and recommendation serving infrast

ructures,* Familiarity with Generative AI applications for search, personalization, product discovery, or shopping as

sistants,* Experience working with fashion, retail, apparel, or lifestyle-related machine learning appl

With over e-commerce operations in 14 countries,1300 stores worldwide and a strong presence in 60 countries, we embark on a great journey embracing the philosophy that 'Everyone Deserves to Dress Well.' Beyond the world of fashion, we are also pursuing significant goals in technology and digital transformation. At LC Waikiki's Digital Transformation and IT Department, we're shaping the future with over 850 experts, focusing on innovation and collaboration. Using Agile/Scrum methodologies, we strive to become a more efficient and dynamic team every day.