Senior Staff Data Scientist
New
J
JobgetherAI Security
Based in CanadaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- At least 5 years
- Required Skills
- PythonSQLCybersecurityData MiningMachine LearningSnowflakeSpark
Requirements
- Advanced degree in Computer Science, Statistics, Applied Mathematics, or another quantitative discipline, or equivalent practical experience.
- At least 5 years of experience applying machine learning or AI to areas such as fraud detection, anomaly detection, cybersecurity, or related problems.
- Strong programming skills in Python and SQL, with experience using modern data-processing frameworks.
- Demonstrated fluency with AI-assisted programming tools such as Claude Code, Cursor AI, or comparable technologies.
- Strong knowledge of statistical modeling, machine learning, data mining, and time-series techniques.
- Deep understanding of anomaly detection, including the statistical challenges associated with sparse and highly unbalanced datasets.
- Experience working with large-scale datasets and distributed computing environments such as Spark or Snowflake.
- Strong analytical and problem-solving abilities, with the capacity to turn complex data into practical security insights.
- Excellent communication skills and the ability to explain sophisticated technical concepts to non-technical stakeholders.
- Experience implementing anomaly detection or AI Security evaluation systems at scale is highly desirable.
- Experience with AI red-teaming tools and knowledge of cybersecurity principles and common attack vectors are strong assets.
- Familiarity with ML model monitoring, production maintenance, guardrails, firewall models, and their impact on customers is an advantage.
Responsibilities
- Develop and operate AI Security evaluation platforms used to assess AI-powered features and identify potential risks.
- Analyze large volumes of behavioral and user interaction data to uncover patterns associated with abuse, anomalies, and security threats.
- Automate analytical methodologies and develop scalable approaches for identifying suspicious activity.
- Design and implement detection models and techniques using offline data to inform production engineering decisions.
- Improve existing spam and abuse detection capabilities while maintaining a strong focus on minimizing false positives.
- Partner with security, product, engineering, and research teams to translate data-driven findings into effective prevention strategies.
- Monitor developments and emerging trends in AI Security and incorporate relevant techniques into research and detection approaches.
- Provide technical leadership across data science initiatives and mentor other team members on complex projects.
- Advise cross-functional stakeholders on emerging data challenges, model performance, and appropriate analytical approaches.
- Contribute to the development of scalable security solutions that protect customers and reduce product abuse.
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