Stefanini LATAM
Stefanini LATAM
Stefanini LATAM is a technology solutions provider focused on digital transformation and business modernization. With more than 30 years of experience, the company helps organizations improve operational models and customer experiences through automation, artificial intelligence, cybersecurity, and data analytics. Its work combines consulting with technology implementation, supporting clients as they identify opportunities to optimize processes, respond to changing market conditions, and pursue sustainable growth.

Data Scientist, Search Relevance - Santiago (Hybrid)

Data Scientist role focused on improving search relevance and ranking for Stefanini’s global technology services. Use machine learning, Elasticsearch, and Learning to Rank to enhance search quality and user experience.

Description

  • Lead the analysis and design of relevance models for the search engine
  • Apply data science and machine learning techniques to improve discoverability
  • Optimize result ranking and strengthen the search experience
  • Evaluate search-engine performance and identify improvement opportunities
  • Analyze historical search data, including queries, clicks, conversions, and purchases
  • Design hybrid lexical and semantic ranking models, including Reciprocal Rank Fusion (RRF)
  • Build training datasets for Learning to Rank models
  • Define and monitor ranking-quality metrics such as NDCG and MRR
  • Partner with the Search Engineer to implement analysis-driven improvements

Requirements

  • Background in data science and machine learning
  • Experience with statistical analysis and data modeling
  • Knowledge of Elasticsearch and search engines
  • Proficiency in Python, SQL, and data analysis
  • Strong analytical thinking
  • Collaborative approach and results orientation
  • Degree in Computer Engineering, Data Science, Statistics, Mathematics, or a related field
  • Experience applying data science and machine learning to large datasets
  • Knowledge of search engines, Elasticsearch, Learning to Rank, and model evaluation metrics

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