Behavioral Scientist

Responsibilities

• Plays a crucial role in ensuring the ethical, responsible, and compliant development and deployment of AI systems, particularly those that impact human behavior.
• Use artificial intelligence (AI) and machine learning (ML) to analyze user and entity behavior within a network to identify and mitigate cyber threats.
• Design and implement behavioral analytics models to surface anomalies and indicators of insider risk across multiple structured and unstructured data sources.
• Develop and refine risk scoring algorithms which prioritize users or events based on observed patterns, contextual risk factors, and deviations from baselines.
• Integrate and transform data from tools, systems, and logs to create unified risk models which support investigative workflows.
• Analyze data to identify patterns, develop theories and models to explain behavior, and design interventions to promote positive change.
• Identify unusual employee or contractor activity that could indicate malicious intent.
• Gather and prepare diverse behavioral datasets, ensuring accuracy and completeness from sources like website interactions, app usage, customer feedback, and other digital channels.
• Conduct exploratory data analysis to identify behavioral trends and precursors which correlate with increased risk profiles.
• Create reports, dashboards, and visualizations to clearly communicate findings and insights to stakeholders, including non-technical audiences
• Enhance IRO’s ability to surface anomalies, prioritize potential risks/threats, and provide decision support.

Technical Skills

• Strong analytical and problem-solving abilities. The ability to identify and assess potential behavioral risks in AI systems, analyze data, and develop effective mitigation strategies is crucial.
• Proficiency in relevant tools. Familiarity with AI frameworks (like TensorFlow, PyTorch), cloud platforms (like AWS), and programming languages (like Python) can be highly beneficial.
• Strong analytical and communication skills are vital to effectively navigate the complexities of the AI evolving field.
• Apply statistical modeling, machine learning, and data fusion techniques to diverse cybersecurity and organizational data.
• Proficiency in languages like Java, C++, Python, and SQL.
• Strong statistical and analytical foundation, including knowledge of anomaly detection, time-series analysis, and clustering techniques.
• Knowledge & experience with API development and automation
• Familiarity with cybersecurity data sources and insider threat indicators, especially UEBA, DLP, system logs, and identity data.
• Problem-solving Skills and a knack for using data to address business challenges and identify opportunities for improvement.
• Data visualization skills using tools like Tableau, Power BI, or Ploy for communicating insights to non-technical stakeholders.
• Evaluate programs, disseminating findings, consulting and collaborating with others, and adhering to ethical standards.

Preferred Qualifications

• A degree from an accredited College/University in the applicable field of services is required. If the individual’s degree is not in the applicable field then four additional years of related experience is required.
• A minimum of eight (8) years’ relevant experience.
• Experience in risk management, compliance, or a related field, with specific experience in AI or machine learning.
• 3+ years of experience in AI concepts and technologies. This includes familiarity with machine learning algorithms, data processing, and AI system design.
• Certifications focused on AI security, governance, and development.

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