Senior Data Scientist - Cybersecurity

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist - Cybersecurity based in Brazil. This role sits at the intersection of data science, natural language processing, and AI security, with a strong focus on building reliable machine learning solutions. You will work hands-on across data curation, model development, evaluation, optimization, and production deployment for AI security use cases. The position focuses on identifying risks in AI interactions, including sensitive-data exposure, unsafe content, adversarial inputs, and malicious prompts. You’ll work with transformer-based models and large language model technologies while developing high-quality datasets and rigorous evaluation frameworks. The role combines experimentation and practical production engineering, with particular attention to accuracy, robustness, latency, and efficiency. You’ll collaborate with AI and engineering teams to turn research and experimentation into scalable solutions that address evolving AI-related threats.

Build, curate, clean, and maintain high-quality 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 for identifying sensitive or relevant information. Design evaluation pipelines, benchmarks, and experiments to measure model accuracy, quality, reliability, false-positive rates, and overall performance. Experiment with different modeling approaches and continuously improve solutions as new 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 reliable production solutions. Contribute to practical AI/ML workflows, technical documentation, evaluation practices, and engineering best practices. Requirements: 4+ years of professional experience as a Data Scientist, Machine Learning Engineer, or in a closely related role. Strong hands-on experience with Natural Language Processing (NLP), transformer-based models, and modern language-model architectures. Strong programming skills in Python. Experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, or comparable technologies. Practical experience with machine learning libraries and ecosystems such as Hugging Face and scikit-learn. Hands-on experience training, fine-tuning, or adapting machine learning models for real-world applications. Experience with NER, tokenization, text classification, or other relevant NLP techniques. Strong data preparation and analysis skills using Pandas or similar tools. Experience designing model evaluations, benchmarks, and experiments to assess model quality and reliability. Understanding of model performance optimization and inference optimization techniques. Familiarity with AI security concepts, including prompt injection, sensitive-data or PII exposure, adversarial inputs, and data privacy. Strong analytical and problem-solving skills, with the ability to investigate ambiguous machine learning problems independently. Ability to collaborate effectively with AI and engineering teams while maintaining a practical, production-oriented mindset. Benefits: 100% remote work, allowing you to work from the location where you are most productive. Competitive compensation paid in USD. Paid time off to support rest, wellbeing, and work-life balance. Flexible autonomy to manage your working time based on results and deliverables. Opportunity to work on innovative, high-impact AI and cybersecurity projects for leading U.S. companies. Hands-on exposure to NLP, transformer models, LLMs, AI security, model evaluation, and production machine learning. Opportunity to work on evolving AI security challenges involving prompt injection, sensitive-data protection, adversarial inputs, and risk detection. Collaboration with experienced technical professionals across a diverse, global network. Multicultural and distributed working environment spanning Latin America and other international markets. Opportunity to collaborate with senior technology professionals and expand your technical and professional network.

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