Senior Data Scientist - Cybersecurity

New

JobgetherAI cybersecurity

Based in BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 4+ years of professional experience as a Data Scientist, Machine Learning Engineer, or in a closely related role.
Required Skills: PythonPyTorchPandasTensorflowscikit-learn

Have 4+ years of professional experience as a Data Scientist, Machine Learning Engineer, or in a closely related role.
Bring hands-on experience with NLP, transformer-based models, and modern language-model architectures.
Have strong programming skills in Python.
Have experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, or comparable technologies.
Have practical experience with machine learning libraries and ecosystems such as Hugging Face and scikit-learn.
Have hands-on experience training, fine-tuning, or adapting machine learning models for real-world applications.
Have experience with NER, tokenization, text classification, or other relevant NLP techniques.
Have data preparation and analysis skills using Pandas or similar tools.
Have experience designing model evaluations, benchmarks, and experiments to assess quality and reliability.
Understand model performance and inference optimization techniques.
Be familiar with AI security concepts, including prompt injection, sensitive-data or PII exposure, adversarial inputs, and data privacy.
Be able to investigate ambiguous machine learning problems independently and collaborate with AI and engineering teams.

Build, curate, clean, and maintain datasets for NLP and AI security applications, including safe and unsafe prompts, sensitive-data exposure scenarios, and adversarial examples.
Generate and augment synthetic datasets using large language models to address edge cases and improve model robustness.
Train, fine-tune, adapt, and evaluate NLP and transformer-based models for real-time risk detection and classification.
Develop and improve custom classification and Named Entity Recognition (NER) models.
Design evaluation pipelines, benchmarks, and experiments to measure model accuracy, quality, reliability, false-positive rates, and performance.
Experiment with modeling approaches and improve solutions as use cases and AI-related security threats emerge.
Optimize models and inference workflows for production environments where low latency and computational efficiency are critical.
Analyze datasets and model performance to identify gaps, edge cases, failure patterns, and opportunities for improvement.
Collaborate with AI and engineering teams to transition models and experiments from research into production solutions.
Contribute to AI/ML workflows, technical documentation, evaluation practices, and engineering best practices.

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