• Build and own the architecture for self-serve and automated insight delivery — from data pipelines to dashboards, alerts, and agents. • Create scalable and reusable dashboards to support forecasting, marketing performance, LTV analysis, product pricing, investment decisions, and KPI reporting. • Develop data tools and intelligent agents that unlock self-serve access to core company metrics across business units. • Translate ambiguous business needs into technical solutions that are resilient, extensible, and intuitive. • Define and enforce data quality standards, governance best practices, and documentation. • Define and standardize product analytics events in collaboration with Product and Engineering (e.g., onboarding steps, feature usage, paywall views.) • Own instrumentation frameworks to ensure all shipped features have measurement plans at launch. • Support A/B testing infrastructure, including experiment design, logging strategy, and outcome analysis to product trials, paywalls, and onboarding flows. • Develop LTV attribution by feature exposure, enabling teams to understand the revenue impact of new launches. • Track and analyze user funnels (e.g., D0-D3 onboarding, trial to conversion, subscription retention) across products to identify drop-offs and optimization opportunities. • Automate impact reporting (e.g., “Feature X led to +6% retention lift among power users”). • Build blended dashboards that unify product, marketing, and monetization metrics in a single view (e.g., how usage ties to ROAS, how features affect CAC payback).

• Analyze data to support or disprove a thesis, letting evidence guide conclusions over confirmation bias • Select and implement the right tools for each problem, from gradient boosting models to transformer-based approaches • Build, train, test, and validate models — from algorithm selection through hyperparameter tuning and rigorous evaluation • Engineer models into production so they run reliably on real infrastructure, serving real customers • Document models, testing protocols, and decision rationale for the team • Monitor and improve models in production, knowing when to retrain, rebuild, or rethink as data and performance drift • Explore agentic and reasoning systems, helping the team separate what's genuinely useful from hype in semi-autonomous, planning AI • Other duties as assigned or apparent

• Formulate, experiment with, and articulate alternative statistical frameworks • Apply rigorous experimental design, hypothesis testing, and statistical analysis to massive federal datasets, validating results against strict requirements and security assumptions • Design, build, and deploy end-to-end machine learning pipelines (including predictive modeling, anomaly detection, and NLP), aligning model development life cycles with the Agile SAFe v6.0 roadmap • Author the scripts and frameworks for modernizing legacy analytics systems into highly distributed, cloud-native processing and intelligence solutions specifically optimized for AWS GovCloud • Establish standardized methodologies for data extraction, manipulation, and feature engineering across enterprise-grade relational and non-relational datastores • Partner with data engineers to design and integrate ML models directly into high-volume, low-latency batch and stream processing pipelines (e.g., Kafka, Spark) that effortlessly bridge application and analytics tiers • Serve as the principal data science advisor across the entire program • Work with software engineers, data architects, and customer application experts to evaluate technical trade-offs and drive advanced analytics capabilities • Leverage Jira, Confluence, and SAFe processes to translate high-level mission capabilities and agency mandates into foundational analytical epics, technical features, and modeling roadmaps

• Understanding the flow of data across the University's different systems, including which fields can be found where, how the data are formatted, and the informations the fields should be capturing • Applying advanced ETL knowledge to support the accuracy, consistency and efficiency of integrated data reporting • Analyzing the data to generate actionable insights • Effectively communicating findings to stakeholders • Building relationships with different departments to ensure that data needs across the University are being met