Cientista de Dados Senior
New
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonElasticSearchMachine LearningData engineeringData scienceA/B testing
Requirements
- Degree in Statistics, Mathematics, Computer Science, Engineering, or related fields.
- Solid experience as a Senior Data Scientist working with production-grade machine learning models.
- Strong knowledge of recommendation systems, ranking models, similarity models, clustering, and applied machine learning techniques.
- Hands-on experience with ElasticSearch, particularly in search relevance and ranking optimization.
- Advanced proficiency in Python and data science / machine learning libraries.
- Experience designing and evaluating experiments, including A/B testing and statistical performance metrics.
- Experience working with large-scale data environments and complex datasets.
- Strong communication skills, with the ability to engage both technical and non-technical audiences.
- Strategic mindset with strong business orientation and problem-solving abilities.
- Proactive, collaborative, and capable of influencing cross-functional teams.
Responsibilities
- Develop, evolve, and maintain search and recommendation systems, working with ElasticSearch and collaborating with internal and external technical teams.
- Design, build, validate, and deploy machine learning models focused on personalization, ranking, similarity, clustering, and recommendation use cases.
- Improve algorithms supporting search relevance, product ranking, collections, cart experience, and CRM personalization strategies.
- Define and execute experimentation frameworks, including A/B testing and offline/online evaluation metrics for model performance.
- Collaborate with Product, Engineering, Data Engineering, and Business teams to define hypotheses, KPIs, and data-driven experiments.
- Translate complex analytical results into clear, actionable insights for technical and non-technical stakeholders.
- Contribute to the definition of data science best practices, architecture guidelines, and documentation standards.
- Support the evolution of the data science function, helping shape roadmaps and fostering a data-driven experimentation culture.
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