Data Science Sênior
New
J
JobgetherData Science
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonSQLMachine LearningAzureDeep LearningNLPGenerative AI
Requirements
- Professional experience using Python for data manipulation, automation, and analytical solution development.
- Strong knowledge of SQL for extracting and transforming data from relational databases.
- Knowledge of statistical modeling and machine learning techniques.
- Experience with Scikit-learn, Streamlit, and Matplotlib/Pyplot.
- Knowledge of TensorFlow and PyTorch.
- Knowledge of NLP, Generative AI, LLMs, and Retrieval-Augmented Generation (RAG).
- Experience with cloud computing environments, particularly AWS and Azure.
- Experience working with notebooks and data science platforms such as Jupyter and Databricks.
- Experience with experimentation and machine learning lifecycle tools such as MLflow.
- Knowledge of statistical libraries including Statsmodels and SciPy.
- Experience applying supervised, unsupervised, and deep learning techniques.
- Strong communication skills for collaborating with both technical and business stakeholders.
- Ability to present technical results and analytical insights clearly to non-technical audiences.
Responsibilities
- Develop predictive models and machine learning algorithms to solve complex business challenges and generate measurable impact.
- Explore, analyze, validate, and interpret data to identify relevant patterns, insights, and opportunities.
- Extract, transform, and prepare data from relational databases using SQL.
- Develop analytical solutions, automations, and data manipulation processes using Python.
- Apply statistical modeling and machine learning techniques to business and data analysis.
- Use machine learning, deep learning, and data visualization libraries and frameworks to build analytical solutions.
- Develop solutions involving Natural Language Processing, Generative AI, LLMs, and Retrieval-Augmented Generation (RAG).
- Act as an interface between business and technology teams, translating business needs into analytical solutions.
- Present analytical results, findings, and recommendations clearly to non-technical audiences.
- Support data-driven decision-making by connecting analytical insights with business objectives.
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