• Lead the overall technical vision for forecasting, metrics ecosystems, audience insights, and transmedia research. • Own the development and operation of robust and interpretable models for pre-launch and post-launch forecasting. • Empower the team to own metrics ecosystem, audience insights, and transmedia research, enabling the Netflix Games’ objectives as the business grows and evolves. • Serve as the most senior technical resource, mentoring the team and fostering data science excellence. • Act as a technical bridge and strategic thought partner to Games leadership, Finance & Strategy, Engineering, and sister DSE teams, ensuring production-grade data products. • Proactively synthesize deep technical insights into clear, actionable findings for executive and senior leadership. • Autonomously identify and pursue the high-impact research, making compelling, data-driven cases for resource allocation and prioritization across the portfolio space.

• Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics. • Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data). • Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse. • Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques. • Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning. • Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust. • Contribute to team standards for ML explainability, risk evaluation, and feature logging. • Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences. • Mentor junior data scientists and participate in cross-functional working groups.

• Solve real business problems with data: Own end-to-end data science work, from problem framing and data exploration to modeling, validation, and production impact. • Lead high-impact initiatives: Drive complex efforts such as vendor leakage detection and prevention, delivering measurable revenue or cost improvements. • Innovate on methods and approaches: Go beyond standard analyses by developing new metrics, models, or workflows when existing approaches fall short. • Build and productionize models: Design, build, and support production ML or LLM-powered solutions in collaboration with engineering and product partners. • Work deeply with data: Use SQL fluently to explore large datasets, build reliable data assets, and validate results. • Influence cross-functionally: Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans. • Set technical direction (especially at L7): Raise the bar for data science quality, scalability, and impact across the organization through mentorship, design reviews, and technical leadership.

• Translate medical policy into executable logic • Build robust clinical feature pipelines • Own measurement, evaluation, and continuous quality improvement • Establish engineering frameworks and tooling • Partner deeply with domain experts