• Design, develop, and deploy LLM- and RAG-powered applications that enhance analyst and hacker productivity across offensive security use cases. • Integrate Generative AI models with internal APIs and security datasets to automate and augment workflows. • Build and fine-tune ML models for vulnerability prediction, triage prioritization, and exploit pattern detection. • Develop evaluation pipelines and feedback loops to continuously improve AI model performance and explainability. • Architect and maintain large-scale, high-performance data pipelines to process vulnerability, asset, and activity datasets from multiple sources. • Build secure data ingestion, transformation, and storage workflows leveraging AWS and modern MLOps practices. • Develop robust CI/CD pipelines for data and ML model deployment. • Collaborate with security researchers and engineers to translate offensive security workflows into data-driven automation.

• Drive product innovation focused on our Games vertical and help shape the member experience • Establish strong partnerships with stakeholders to realize our vision for Games • Develop experimentation and measurement frameworks to increase the velocity of investments • Facilitate ownership and accountability by ensuring the team produces trustworthy, high-quality outputs • Be flexible to changing circumstances and shape Netflix Games' future

• Lead or support data management study activities, CRO oversight, and driving deliverable timelines. • Strong knowledge of EDC builds. • Represents data management function on the Clinical Sub-team ensuring aligned expectations between the CRO and client for all data related deliverables, especially in support of key decision points and regulatory submissions. • Contributes influential leadership in collaboration with other client Stakeholders to ensure established milestones and deliverables are met with the highest degree of quality. • Partners with appropriate stakeholders and CRO partners to mitigate and resolve risks. • Provides input to functional governance with client strategic suppliers. Partners with appropriate stakeholders to resolves issues escalated from the vendor and/or cross-functional teams. • Participates in preparing function for submission readiness and may represent function in a formal inspection or audit. • Participates and represents function in formal inspections and audits as requested. • Ensure archival and inspection readiness of all Data Management Trial Master File (TMF) documents. • Ensures achievement of major data management deliverables and milestones in coordination with other functions including the Therapeutic Area Units, Clinical Operations, Statistical Programming and Statistics. • Responsible for the planning and management of external Data Management budgets and timelines to ensure accuracy, understand trends in variances and support continuous improvement in forecasting. • Acts as a process expert for operational and oversight models. • Maintains SOPs, process maps and templates and timelines to support functions operational and oversight models. • May prepare metrics to support the function’s KPIs. • Represents function in external professional initiatives and organizations such as SCDM, CDISC, DIA, etc. to identify industry best practice and increase the visibility of client. • Contributes to functional Continuous Improvement initiatives, providing strategic direction and identifying key deliverables that meet timelines, budget, and are in alignment with company, departmental or functional requirements. • Ensure compliance with own Learning Curricula, corporate and/or GXP requirements. • Works cross-functionally to ensure the quality of the data in each database and on time delivery, as well as quality of other data management deliverables.

• Build, validate, and deploy statistical and AI models to solve complex business problems. • Design and refine predictive features, ensuring data quality and model interpretability. • Leverage Azure Machine Learning and Microsoft’s suite of data services to scale model training and deployment. • Apply statistical rigor to analyze data trends and communicate findings to non-technical stakeholders.