Data Scientist – Personalization & Search

LC Waikiki; Fatih; Hybrid; TRY 420,000 - 660,000; Full time

Benefits offered by this job

Hybrid work option at Istanbul office

LC Waikiki product discounts

Training and professional development

Collaborative team culture

Job summary

LC Waikiki is seeking a Mid/Senior Data Scientist to advance personalization, search, and multimodal intelligence for fashion. You will design and deploy scalable recommendation and retrieval systems, working closely with business stakeholders to translate insights into impactful customer experiences.

The role emphasizes collaboration, cutting-edge AI techniques, and ongoing learning within LC Waikiki’s Digital Transformation and IT department, based in Istanbul with a hybrid work option.

Qualifications

• Minimum of 2 years of experience in Data Science, ML, Personalization, or related domains.
• Strong proficiency in Python and modern ML development practices.
• Strong understanding of supervised learning algorithms incl. tree-based methods (LightGBM, XGBoost, CatBoost, Random Forests).
• Hands-on experience designing, training, tuning, and deploying deep neural networks for retrieval and ranking models.
• Experience with TensorFlow, TensorFlow Recommenders (TFRS), PyTorch, or similar frameworks.
• Understanding of recommendation system methodologies: Content-Based, Hybrid, Matrix Factorization, Learning-to-Rank.
• Experience with product affinity and market basket analysis techniques (Apriori, FP-Growth).
• Understanding retrieval architectures: Sparse, Hybrid, Semantic, Vector Search.
• Experience with multimodal embedding models (FashionCLIP, CLIP).
• Experience with vector databases and ANN search (FAISS, Milvus).
• Proficiency in cloud environments (GCP, Azure).
• Experience deploying ML solutions via batch and real-time serving.
• Understanding of MLOps: versioning, monitoring, experimentation, CI/CD.
• Strong SQL skills and handling large-scale datasets.

Responsibilities

• Design and develop personalized recommendation systems and retrieval architectures.
• Build and optimize ranking models for product recommendations and search results.
• Develop retrieval systems using dense, sparse, and hybrid techniques.
• Improve search relevance with semantic and vector search approaches.
• Create pipelines for candidate generation, retrieval, ranking, and diversification.
• Apply association rule mining for cross-sell and upsell opportunities.
• Develop ML solutions for fashion multimodal understanding.
• Collaborate with stakeholders; translate ML findings into business impact.
• Stay updated with advancements in recommender systems and AI.

Skills

Python

SQL

Machine Learning

Recommender Systems

Vector Search

A/B Testing

Communication

Tools

LightGBM

XGBoost

CatBoost

TensorFlow

PyTorch

FAISS/Milvus

Job description

Are You Ready to Join LC Waikiki’s Digital Transformation Journey?

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.

We are looking for Mid/Senior Data Scientist – Personalization & Search

What We Offer:

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 you:

Personalization, Search and Recommendation Systems

• Design and develop recommendation systems using collaborative filtering, content-based filtering, hybrid recommendation approaches, and deep learning-based retrieval architectures.


• Build and optimize personalized ranking models for product recommendations, search result ranking, and merchandising use cases.


• Develop retrieval systems leveraging dense, sparse, and hybrid retrieval techniques.


• Implement and improve search relevance using semantic search, vector search, and multimodal retrieval approaches.


• Design recommendation pipelines including candidate generation, retrieval, ranking, re-ranking, diversification, and business-rule optimization stages.


• Apply association rule mining and frequently-bought-together techniques to discover product affinities and improve cross-sell and upsell experiences.

Fashion AI and Multimodal Intelligence

• Develop machine learning solutions utilizing text, image, and behavioral signals to improve personalization and product discovery experiences.


• Work with multimodal embedding models such as FashionCLIP, FashionSigLIP, CLIP, SigLIP, and similar architectures for fashion understanding tasks.


• Build semantic similarity systems for product matching, visual search, outfit recommendation, and catalog enrichment.


• Explore emerging multimodal AI techniques to enhance customer engagement and product discoverability.

Stakeholder Management

• Act as a key partner for business stakeholders on personalization and search initiatives.


• Translate complex machine learning concepts into actionable business recommendations.


• Communicate model performance, experimentation results, and business impact to technical and non-technical audiences.


• Present findings and recommendations to senior leadership and product teams.

Innovation and Research

• Stay up to date with advancements in recommendation systems, search technologies, deep learning, and Generative AI.


• Evaluate and prototype emerging methodologies, architectures, and tools to improve personalization capabilities.


• Contribute to innovation initiatives by identifying and testing new opportunities in AI-driven customer experiences.

Who Are We Looking 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 experiences.

Required Qualifications

• 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 practices.


• 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 use cases.


• Experience with TensorFlow, TensorFlow Recommenders (TFRS), PyTorch, or similar deep learning frameworks.


• Comprehensive understanding of recommendation system methodologies including:


• Content-Based Filtering


• Hybrid Recommendation Systems


• Matrix Factorization


• Learning-to-Rank Approaches


• Experience with product affinity and market basket analysis techniques including:


• Apriori


• FP-Growth


• Association Rule Mining


• Frequently Bought Together Systems


• Strong understanding of retrieval architectures including:


• Sparse Retrieval


• Hybrid Retrieval


• Semantic Search


• Vector 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 architectures.


• Experience working with vector databases and ANN search technologies such as FAISS, Milvus, Vertex AI Vector Search, Azure AI Search, Pinecone, Weaviate, or similar platforms.


• Proficiency in cloud environments such as Google Cloud Platform and Microsoft Azure.


• Experience deploying machine learning and recommendation solutions through batch and real-time serving architectures.


• Understanding of MLOps concepts including model versioning, monitoring, experimentation tracking, and CI/CD workflows.


• Strong SQL skills and experience working with large-scale datasets.


• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• 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 architectures.


• Experience with A/B testing frameworks and online experimentation methodologies.


• Experience with feature stores, real-time feature engineering, and recommendation serving infrastructures.


• Familiarity with Generative AI applications for search, personalization, product discovery, or shopping assistants.


• Experience working with fashion, retail, apparel, or lifestyle-related machine learning applications.

What’s Waiting for You:

• Hybrid Work Option: At our modern office in Giyimkent, Istanbul.


• A team culture focused on collaboration and inclusivity.


• The chance to work with cutting-edge technology and tools.


• Ongoing training and professional development opportunities.


• Discounts on LC Waikiki products and much more!

At LC Waikiki, we are committed to considering our environmental and social impacts throughout our value chain while developing and maintaining an ethical, responsible, and sustainable business model that respects human rights.

The person in this role is expected to consider not only the balance between cost, quality, and speed in decisions and practices within their area of responsibility and influence, but also resource efficiency, the use of sustainable materials, responsible production and procurement practices, respect for human rights, decent working conditions, and ethical business principles. In addition, they are expected to contribute to the identification, management, and monitoring of relevant sustainability impacts, risks, and opportunities.