• Enable our product teams to think strategically with key stakeholders about how customers engage with Webflow, identifying and executing on high impact growth opportunities with actionable insights • Define product and business success metrics, building tools for monitoring and surfacing drivers of KPIs (Key Performance Indicators) • Lead technical and non-technical team members with processes and technical expertise for executing experiments, answering open ended business questions with data, and researching levers for improving our product. • Develop our data foundation, experimentation and machine learning capabilities, collaborating on our strategic roadmap for building new pipelines and tools that contribute to our connected, complete understanding of our customer journey and improve our customer experience. We currently use Snowflake, DBT, Tableau, and Fivetran and are working with our Data Engineering collaborators on scaling our platform capabilities with a number of other tools!

• 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.