Data Specialist

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 indiviual'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.