Education Resource Strategies

• Lead ML model creation, support foundational standardization • Create machine learning model architecture, parameters, and related technical specifications to accurately classify education finance data to a common reporting structure. • Guide staff in the sourcing, preparation of training data for machine learning models that map local accounting codes to standardized national categories. • Develop and implement model features derived from raw financial records, metadata, and related datasets. • Lead model iteration, evaluation, and improvement process with technical team members in support. • Provide analytical support in translation process. • Support processes and statistical rules for transforming current federal data into a nationally comparable, complete, and actionable dataset by identifying opportunities for efficiency and accuracy improvements. • Address reporting discrepancies—such as varying state treatments of teacher pensions and debt—to create a standardized foundational dataset.

• Lead strategy and execution for building SPEND's organic audience across owned digital channels, with a focus on direct cultivation rather than traditional media outreach. • Develop and maintain a content cadence and editorial calendar that keeps SPEND publishing consistently and purposefully. • Build and segment SPEND's distribution list, ensuring we are reaching the right audiences—researchers, policy analysts, state and district leaders, journalists, and field partners—with content that is relevant to each. • Support SPEND's positioning as a technical subject matter expert through earned media and direct channel publishing. • Optimize SPEND's content and web presence for discoverability in both traditional search engines and AI-generated results—understanding that search increasingly means appearing in LLM responses, not just blue links. • Develop style guides, content templates, and review processes so that all external outputs reflect a consistent, recognizable SPEND voice. • Collaborate closely with the team to develop communications materials that reflect SPEND's objective, non-partisan posture as a technical subject matter expert—not an advocacy organization or rapid-response quote machine.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description SPEND is seeking a Senior Data Analyst to support the development, validation, and analysis of the next generation of education finance data. The Senior Data Analyst will serve as a cross-cutting team member contributing to SPEND’s: - Continuity and enrichment efforts - Cutting-edge AI pilot model development through the development and execution of quantitative analyses The ideal candidate will have previous experience in both data analytics and education finance to ensure organization strategy and methods properly account for the current data landscape, the perspectives of data providers, and the needs of data users. Key Responsibilities “Eastbound” Track (Enhancing the Present) - Focus: Dataset Curation, Organization, Standardization and Process Validation - Lead on QA/verification, support on statistical alignment & process work - In collaboration with senior technical staff, build internal data systems and ingestion processes through ETL/ELT pipelines. - Create a validation system to compare newly processed data against historical benchmarks to ensure longitudinal consistency. - Support Senior Associate Data Scientist to create and implement processes to transform current federal data into a nationally comparable, complete, and actionable dataset. - Build and maintain quality assurance processes that identify anomalies and outliers in district- and state-level financial data. - Deepen current data by accurately and completely incorporating long-term obligations such as pension and facilities expenditures. - Implement processes to correctly incorporate contributions from sub-contractor subject matter experts. “Westbound” Track (Building the Future) - Focus: Data System Management, Improvement, and Alignment - Support Senior Associate Data Scientist and ML Engineer in model creation through data system support. - Support creation and maintenance of training data for machine learning models that map local accounting codes to standardized national categories. - Manage the manual validation of the AI/algorithm outputs, checking automated results against real-world district spending data. - Evaluate model outputs and conduct error analysis in collaboration with the technical team. - Perform deep-dive analysis on finalized datasets to identify and resolve anomalies before public release. Cross-Cutting Contributions - Document analytical methods, assumptions, and validation results clearly and reproducibly. - Contribute to development, maintenance, and improvement of cloud infrastructure to support short-term and long-term objectives. - Develop internal and public-facing methodological documentation to build trust in, and encourage use of SPEND data products across audience groups. - Regularly collaborate with team members (scientists, engineers, etc.) and external stakeholders (policy experts, state and district leaders, etc.) to advance the organizational mission. Qualifications - Bachelor’s in Data Science, Computer Science, Public Policy, or a related field. - Working knowledge of Python, SQL, or R and familiarity with modern cloud data infrastructure. - Demonstrated experience working as part of a high-performing technical team. - Experience translating analytical models and findings into clear policy insights. - Experience working with messy and inconsistently structured datasets. Requirements - Must be available for core working hours of 10 AM – 5 PM ET to support national team collaboration. - Ability to travel 4–6 times per year for in-person team collaboration, with approximately 2–3 additional trips per year for external partner engagement. - Ability to operate a computer and other office productivity machinery for extended periods; ability to remain in a stationary position for a significant portion of workday; ability to communicate via video conference. - Access to a quiet, professional remote work environment with a reliable, high-speed internet connection. - Must be authorized to work for an employer in the United States. We are unable to sponsor or take over sponsorship of an employment visa at this time. Preferred - Advanced degree (Master's or PhD). - Experience with public sector data systems or education finance reporting structures. Compensation and Transparency The salary range for this role is $90,000 to $110,000 annually. Salaries will be adjusted for geography based on the candidate's primary work location. ERS equivalent job tier for benefits purposes: Level II. Company Description ERS is a national non-profit consulting firm that partners with school system leaders to transform how they use resources so that every student can learn and thrive. ERS is an equal opportunity employer and does not discriminate on the basis of race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, ancestry, pregnancy, or military service.