• Lead the design and implementation of causal inference frameworks (e.g., uplift modeling, DML, IVs, DiD, synthetic control) to measure true incremental impact across personalization, marketing, and lifecycle interventions • Establish and standardize methodologies for incrementality, experimentation, and measurement across channels and product surfaces • Build and scale LTV models (user-level and cohort-based), including churn-adjusted and horizon-specific approaches, for real-time decisioning • Develop and deploy personalization models that influence ranking, offer selection, content sequencing, and monetization strategies at the moment of user intent • Ship production-grade machine learning models that directly drive revenue outcomes, including marketplace optimization, partner routing, and budget allocation • Translate predictive outputs (e.g., conversion propensity, incremental CPA, expected LTV) into decision-ready signals for real-time systems • Partner with Data Engineering and Platform teams to define data instrumentation, feature stores (batch and streaming), and model monitoring frameworks (drift, bias, stability) • Influence architectural decisions across modern data and ML platforms (e.g., Snowflake, Databricks, Spark, real-time inference systems) • Provide technical leadership across teams by setting best practices for experimentation, modeling, code quality, and reproducibility • Mentor and develop senior and mid-level data scientists, raising the overall technical bar across the organization • Communicate complex analytical insights and trade-offs to executive stakeholders, translating findings into actionable business strategies • Shape long-term strategy for personalization, experimentation, and AI-driven growth at NerdWallet

• Drive GTM with Data • Build frameworks to identify and prioritize opportunities • Develop scoring approaches to guide GTM focus • Translate on-chain activity into clear, actionable insights • Support GTM decision-making with data • Support Sales Execution • Embed into sales workflows and engagements • Develop bespoke data narratives to strengthen pitches • Support live deals with relevant insights • Use data to differentiate in competitive opportunities • Expand Data Foundations • Build on existing data foundations to support GTM needs • Identify gaps in available data and work with data teams to address them • Ensure outputs are clear and usable for GTM teams • Translate Data into Action • Move from raw data to clear points of view • Deliver actionable recommendations, not just analysis • Connect on-chain signals with business context

• Diseñar y desarrollar modelos de machine learning e IA para casos de negocio. • Entrenar, evaluar y desplegar modelos utilizando Amazon SageMaker. • Implementar soluciones de IA generativa con Amazon Bedrock. • Preparar y transformar datos asegurando calidad y disponibilidad. • Colaborar con equipos para llevar modelos a producción bajo buenas prácticas de MLOps.

• Rapidly stitch together estimates of relevant variables from data you can rapidly obtain (e.g. health harm caused by different Climate TRACE assets) • Iteratively improve estimates by continuously improving algorithms with new input data using Bayesian updating or similar methods • Pull, clean, format, and organize relevant data from the web, satellites, our databases, and partners • Write clear methodologies, API documentation, and papers for technical audiences • Write brief punchlines or blog posts for team members, policymakers, nonprofits, and corporate decision makers • Estimate the causal impact of various interventions (like building solar) • Quantify opportunities to increase impact by adjusting control variables (e.g. moving air quality sensors to where it will minimize GHGs) • Conduct formal analysis of the comparative shadow value of relaxing different control variable constraints across many complex causal chains • Apply this to inform the team’s data assimilation work: where would improving our data or methods cause the most impact?