3007 - Data Scientist - Machine Learning Engineer -PL-RE-248158


Descrição de Vaga

Código: 3007
Título da vaga: Data Scientist - Machine Learning Engineer -PL-RE-248158
Local: São Paulo,São Paulo
Região: Outra
Tipo de emprego: PJ
Nível Profissional:
NÍvel Acadêmico: Ensino Superior Completo
Habilidades: • Mandatórios

Ph.D. in Data Science, Computer Science or other relevant scientific fields.
5+ years of experience working with NLP and ML technologies.
Proven experience as a Machine Learning Engineer or similar role.
Experience with NLP/ML frameworks and libraries
Proficient with Python scripting language
Background in machine learning frameworks like TensorFlow, PyTorch, Scikit Learn, etc.
Demonstrated experience developing and executing machine learning, deep learning, data mining and classification models.
Proficient with relational databases and SQL
Creative, innovative, and strategic thinking; willingness to be bold and take risks on new ideas.
Implementation expertise in ML models and specialized experience in NLP. "

Inglês Fluente para reporte diário ao cliente nos USA
Remuneração Básica: -   - 
Benefícios: 0
Resumo da Vaga: Atividades

Build, deploy, and test machine learning and classification models
Train and retrain systems when necessary
Machine Learning and data labelling. Identify ways to gather and build training data with data labelling
Automatic extraction of causal knowledge from diverse information sources such as databases, news, social media, etc...
Develop and implement approaches for extracting patterns and correlations from both internal and external data sources using machine learning toolkits.
Develop customized machine learning solutions including data querying and knowledge extraction.
Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviours.
Distil insights from complex data, communicating findings to technical and non-technical audiences.
Contribute to the development of highly accurate training sets.
Develop, improve, or expand in-house computational pipelines, algorithms, models, and services used in crop product development.
Constantly document and communicate results of research on data mining, analysis, and modelling approaches."

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