• 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

• Lead hands-on development and delivery of analytics solutions by architecting and troubleshooting as needed, driving the creation of enterprise data models, data products, analytical datasets, and SAC reporting assets, while prioritizing SAP-delivered content and ensuring adherence to best practices and fit-to-standard principles. • Execute the enterprise analytics strategy by driving the roadmap aligned to business transformation goals, advancing SAP Business Data Cloud and AI-enabled capabilities, establishing architecture and governance standards, mentoring technical teams, and partnering with business stakeholders to deliver scalable analytics solutions. • Design end-to-end analytics solutions by creating scalable architectures across SAP Business Data Cloud, Datasphere, SAC, S/4HANA Embedded Analytics, BTP, and intelligent data products, while developing robust data models, establishing governed semantic layers, and enabling seamless integration between SAP and non-SAP data sources. • Collaborate with business stakeholders to gather requirements, translate them into technical designs, and deliver high-quality solutions. • Optimize performance, scalability, and security of analytics platforms. • Drive adoption of new SAP BDC features, staying ahead of industry trends and advising on best practices. • Drive enterprise-wide adoption of common KPIs, business definitions, and semantic models. • Ensure compliance with security, privacy, and regulatory requirements. • Provide technical leadership, mentoring, and guidance to developers and analysts. • Own IT service delivery for assigned manufacturing sites, maintaining strong relationships and ensuring operational excellence. • Collaborate cross-functionally to deliver IT services aligned with business needs during organizational transitions, including separation activities. • Participate in the design and implementation of secure, scalable, and reliable technology solutions that support core business operations and digital transformation. • Ensure manufacturing connectivity and data flow across enterprise systems, enabling efficient production and supply chain processes.

• Contribute to the design and build intelligence systems that help Liquid Engine understand workflow context, track change over time, and support consistent outputs • Translate creative and production workflows into structured representations such as entities, relationships, rules, and graphs • Help develop systems for tracking state, version history, provenance, and dependencies across workflows • Build rule-based and heuristic systems that identify conflicts, evaluate consistency, and surface issues before they create downstream problems • Support change impact analysis by helping the system reason about dependencies, cascades, and workflow consequences • Develop evaluation frameworks that measure system usefulness, output quality, and reduction of manual rework • Partner with engineering on event pipelines, data models, and system architecture that support intelligent system behavior • Contribute to explainable recommendation and decision-support systems grounded in workflow context • Help define telemetry, measurement, and governance approaches for intelligence features across the platform