• This position is responsible for building predictive, forecasting, and optimization models for the system of hospitals at OHSU health. • Designs models necessary to forecast capacity issues facing the institution. • Providing technical leadership for defining and developing advanced analytics products. • Use methods from statistics (regression, survival modeling), econometrics (time series), and AI modeling (LLM or multi-modal). • Develop code that meets standards for reliability and scalability. • Output from the model should be provided in various forms to allow other users across the institution to use them. • Delivering business intelligence products under required timelines and quality standards. • Work with ETL engineers to ensure efficient use of data. • Creating and maintaining a dashboard tool for visualizing and interacting with models. • Analyzing present and future data needs of customers and makes recommendations on options and alternatives for implementing applications, systems, or clinical/operational workflows and processes. • Configuration of output for use in dashboards, databases or other tabular forms. • Assists customers with the development of test plans and testing schedules for new systems and applications. • Contributing to the design of analytic, application, or workflow specifications for the most complex and critical OHSU systems and applications. • Ensuring data integrity and validity of models and systems. • Designs, develops and documents new applications and systems or clinical/operational workflows. • Design data reporting to make modeling results available to customers and other analysts at the institution. • Works with vendors, other technical staff and customers to design, develop, test and implement interfaces between systems. • Will fully understand and utilize the System Development Life Cycle (SDLC) process and procedures at OHSU. • Will manage projects by creating timelines, identifying risks and milestones, and providing status reporting to others as defined. • Assumes project management responsibilities for small projects or sub-projects within a larger project.

• 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.