Search & Recommendation Data Scientist(Intelligence Planning)

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Drive search and recommendation intelligence by analyzing global user behavior, search/recommendation logs, and content metadata to define problem statements and improvement opportunities. Design statistical/ML model directions and prototypes for personalization, ranking, targeting, conversion prediction, and segmentation. Develop core features and domain variables for retrieval/ranking/recommendation and RAG/LLM search, then establish offline/online metrics and A/B-test evaluation systems.

Location: South Korea

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Analyze global user behavior data, search logs, recommendation logs, and content metadata to define search/recommendation problems and improvement opportunities.
•Design statistical/ML model directions and prototypes for personalization, search ranking, user targeting, conversion-rate prediction, and segmentation.
•Identify core features and domain variables for retrieval, ranking, recommendation, and RAG/LLM search, and define data requirements.
•Analyze differences in user behavior by country/region, data bias, and business context to propose global algorithm deployment strategies.
•Set up offline/online evaluation metrics, experiment design, and A/B-test-based assessment, then connect analysis outputs to development using model specification, feature requirements, and evaluation protocols.

Key Requirements

•Have a Master’s degree or equivalent expertise in quantitative fields such as statistics, mathematics, industrial engineering, econometrics, or computer science.
•Have performed analytics/modeling using large-scale user behavior data or service logs.
•Have modeling experience with one or more of classification, regression, clustering, ranking, and recommendation.
•Understand data-driven validation methods such as statistical hypothesis testing, experiment design, and A/B testing.
•Ability to clarify ambiguous service problems into data analysis, model direction, features, and evaluation criteria.

Education:Master's

Skills:CommunicationProblem-solving

Tech Stack:PythonRSQLPandasSparkA/B testingRAGLLMEmbedding spaceSemantic searchRetrievalRankingRecommendation

Company Brief

42dot

Website

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42dot

1 week ago

Search & Recommendation Data Scientist(Intelligence Planning)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: South Korea

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Analyze global user behavior data, search logs, recommendation logs, and content metadata to define search/recommendation problems and improvement opportunities.
•Design statistical/ML model directions and prototypes for personalization, search ranking, user targeting, conversion-rate prediction, and segmentation.
•Identify core features and domain variables for retrieval, ranking, recommendation, and RAG/LLM search, and define data requirements.
•Analyze differences in user behavior by country/region, data bias, and business context to propose global algorithm deployment strategies.
•Set up offline/online evaluation metrics, experiment design, and A/B-test-based assessment, then connect analysis outputs to development using model specification, feature requirements, and evaluation protocols.

Key Requirements

•Have a Master’s degree or equivalent expertise in quantitative fields such as statistics, mathematics, industrial engineering, econometrics, or computer science.
•Have performed analytics/modeling using large-scale user behavior data or service logs.
•Have modeling experience with one or more of classification, regression, clustering, ranking, and recommendation.
•Understand data-driven validation methods such as statistical hypothesis testing, experiment design, and A/B testing.
•Ability to clarify ambiguous service problems into data analysis, model direction, features, and evaluation criteria.

Education:Master's

Skills:CommunicationProblem-solving

Tech Stack:PythonRSQLPandasSparkA/B testingRAGLLMEmbedding spaceSemantic searchRetrievalRankingRecommendation

Company Brief

42dot

Website