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Data Scientist

Posted 2024-11-21

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💎 Seniority level: Senior, 5+ years

📍 Location: United States

🔍 Industry: Cybersecurity

🏢 Company: GreyNoise Intelligence

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: AWSDockerPythonSQLElasticSearchKubernetesMachine LearningNumpyPyTorchSnowflakeProduct DevelopmentData scienceElasticsearchCollaborationDocumentation

Requirements:
  • 5+ years of data science experience and/or an advanced degree in a relevant discipline (Data Science, Machine Learning, Operations Research, etc.).
  • Experience implementing machine learning techniques with real-world data, preferably in computer networking or cybersecurity; specifically clustering and anomaly detection.
  • Proficiency with machine learning frameworks and libraries such as PyTorch, scikit-learn, and numpy.
  • Experience with natural language processing (NLP) techniques and working with LLMs.
  • Strong programming skills in Python and familiarity with statistical analysis.
  • Experience in data visualization with a variety of tools and working with front-end developers to bring visualizations to production.
  • Familiarity with database and big data technologies (Elasticsearch, SQL, Snowflake, etc.).
  • Knowledge of cloud-based hosting and ML services, particularly AWS.
  • Understanding of containerization and deployment technologies like Docker and Kubernetes.
  • Ability to communicate technical concepts effectively, both to teammates and external audiences.
  • Excellent problem-solving skills and adaptability in a dynamic environment.
Responsibilities:
  • Develop and deploy machine learning models for real-time anomaly detection and threat identification.
  • Automate the discovery of interesting and anomalous data from our global honeypot network.
  • Research and implement new LLM technologies to help read and understand complex internet traffic patterns.
  • Integrate new visualizations and statistical models into our product to enhance user experience and data interpretation.
  • Ensure data quality by collaborating with infrastructure engineers to develop tests and alerts for detecting defects and determining their origin.
  • Optimize data pipelines in collaboration with data engineers for efficient data processing.
  • Interface directly with customers to capture analytical requests and translate them into actionable engineering requirements.
  • Present findings through social media, blogs, and conferences to engage with the broader community.
  • Stay current with the latest AI/ML research and cybersecurity trends to continuously improve our solutions.
  • Monitor and tune ML models in production environments to ensure scalability and reliability.
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