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Talentxo
Machine LearningData Science

Associate Lead - Data Scientist ( Python, AL/ML)

Posted Recently
Full-time

Overview

Develop and optimize enterprise-wide search systems and AI/ML models for ranking, personalization, and recommendations. Collaborate with business units to build AI-enabled search features and improve search accuracy through user behavior analysis.

What You'll Do6

  • 1Contribute to the development and optimization of enterprise-wide search systems and models.
  • 2Design and implement algorithms to improve indexing, query relevance, and search accuracy.
  • 3Support taxonomy, ontology, and metadata model creation for better search outcomes.
  • 4Collaborate with business units to build AI-enabled search features.
  • 5Conduct analysis of user behavior and system metrics to refine search performance.
  • 6Develop production-grade ML systems for ranking, personalization, and recommendations.

Requirements6

  • 15–8 years of experience in Search, Information Retrieval, NLP, and Machine Learning.
  • 2Hands-on experience deploying ML/AI-based systems at scale.
  • 3Strong knowledge of search technologies (indexing, faceted search, NLP-based search).
  • 4Programming skills in Python, Java, or Scala.
  • 5Familiarity with ML/DL frameworks (TensorFlow, PyTorch, scikit-learn, Keras).
  • 6Experience with SQL/NoSQL databases and CI/CD pipelines.

Who Should Apply

An experienced data scientist with 5-8 years in search and NLP, proficient in Python/Java/Scala and ML frameworks, who enjoys building production-grade ML systems and collaborating cross-functionally. The ideal candidate has a strong engineering mindset and experience with CI/CD and databases.

Salary Insight

Compensation is open to discussion.

Required Skills

banking and financeci/cd tools/platformsconsumer internetdata engineering & cloudjavalinuxmachine learning (ml) & artificial intelligence (ai)ml/dl frameworkspythonscalasqltechnology

Application Tip

Highlight your experience with search technologies (e.g., Elasticsearch, Solr) and production ML deployments, and provide examples of how you improved search accuracy or personalization.

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