Data Scientist (Pre-Search)

About the Team

At Trendyol Tech, our mission is to create a positive impact in our ecosystem by enabling commerce through technology.

We solve complex problems with data, creativity, and agility — always driven by real outcomes. With a culture built on learning, collaboration, and ownership, we grow together while building what’s next.

About the Role

Our team builds the language intelligence and retrieval foundations behind Trendyol Search across multiple regions. We focus on helping customers express their intent accurately and discover relevant products quickly, regardless of the language or market they search in.

As a Data Scientist, you will work on high-impact problems at the intersection of NLP, information retrieval, and e-commerce search. You will contribute to multi-regional search models covering spelling correction, query understanding, semantic and vector search, while taking particular ownership in improving the Arabic-language search experience.

Your work will directly improve product discovery for millions of customers across Trendyol's markets.

Responsibilities

Develop and improve query understanding capabilities for Trendyol's search experience across multiple regions and languages, including spelling correction, query normalization, query rewriting, intent detection, and semantic retrieval.

Build and evaluate vector search, embedding, and retrieval models that improve the relevance of product discovery for customers across different markets.

Take a leading role in Arabic-language search initiatives, addressing language-specific challenges such as dialectal variation, orthographic normalization, transliteration, morphology, mixed-language queries, and spelling errors.

Analyze large-scale structured and unstructured data, including search queries, product content, user interactions, and linguistic signals across the Trendyol ecosystem.

Design robust offline evaluation methodologies and quality metrics for query understanding, spelling correction, and retrieval systems; validate improvements through online A/B experiments.

Collaborate closely with Search, Pre-Search, Ranking, Product, and Engineering teams to translate customer and business needs into scalable machine learning solutions.

Improve model quality, latency, reliability, and deployment cycles by applying MLOps best practices to production ML systems.

Monitor model and search-quality performance after deployment, investigate regressions, and continuously iterate based on data and user feedback.

Communicate methodology, findings, and expected business impact clearly to technical and non-technical stakeholders.

Expected Qualifications

Experience in applied data science and machine learning, ideally in NLP, information retrieval, search, or language modeling domains.

Professional working proficiency in Arabic, with a strong understanding of Arabic grammar, morphology, spelling conventions, dialectal variation, and common search-query behavior. Native-level proficiency is a strong plus.

Practical experience with modern NLP methods, including transformer-based language models, text embeddings, semantic search, retrieval, reranking, query understanding, or spelling correction.

Familiarity with Arabic NLP resources, datasets, tokenization or normalization approaches, and evaluation challenges is highly preferred.

Strong Python skills and hands-on experience with relevant ML libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, or similar frameworks.

Strong SQL knowledge and familiarity with data-analysis best practices.

Experience with vector databases, approximate nearest neighbor search, embedding models, or large-scale retrieval systems is a strong plus.

Experience designing and interpreting A/B experiments, offline evaluations, and statistical analyses.

Strong analytical skills, a data-driven mindset, and the ability to work effectively with ambiguous product problems.

Excellent written and verbal communication skills in English. Turkish is a plus.

Experience in e-commerce, marketplaces, search, recommendation, or self-service platforms is a plus.