Education Resource Strategies→
SPEND - Machine Learning Engineer at Education… · Remote
Mid LevelRemoteFull-timeRemote$150k–$180k/yr
Skills
pythonsqlgoogle cloud platformnlpmachine learning
Job Description
Strategic Public Education National Data (SPEND) Initiative
Modernizing America’s Education Finance Data InfrastructureMachine Learning Engineer
Location: Remote (U.S.-based required)About the SPEND Initiative
Accurate and comparable education finance data is the essential foundation for effective governance and robust research. The Strategic Public Education National Data (SPEND) Initiative is a non-partisan organization and a fiscally sponsored project of Education Resource Strategies (ERS). Our mission is to maintain and modernize the technical pipeline that improves the accuracy, comparability, and timeliness of education finance data. We aim to provide a stable, objective data foundation—a modern data utility that serves the needs of researchers, state and district leaders, and the broader education field.Why Work at SPEND?
- Preserve and Upgrade National Data Infrastructure: Your work directly contributes to sustaining the longitudinal school finance datasets that underpin billions of dollars in funding and decades of research on resource allocation and system effectiveness.
- Execute Technical Modernization: Transition the national data infrastructure from fragmented legacy reporting to automated, high-quality systems, establishing an objective foundation for fiscal data collection and transparency.
- Bridge the Analytics Gap: Help solve the “apples-to-oranges” discrepancies in how education finance data is reported, enabling researchers and policymakers to better compare spending across states and districts.
- Join a Mission-Focused Team with Institutional Stability: Contribute as a foundational member of a new initiative supported by a multi-year general operating grant and the established operational infrastructure of ERS.
Role Overview
SPEND is seeking a Machine Learning Engineer to lead the development, validation, and analysis of the next generation of education finance data. The Machine Learning Engineer will serve as a cross-cutting team member contributing to SPEND’s continuity and enrichment efforts and AI pilot model development.You will lead the creation of a "data translation engine", using machine learning to accurately classify records and mitigate the "roll-up effect" that often leads to data degradation in current reporting systems. The ideal candidate will have previous experience in machine learning, data science and experience, or demonstrated interest in, public policy 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
Innovation & Automation (Building the New System) – 80% of timeFocus: ML Model Development
Summary: 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.
Focus: Translation & Improvement Processes
Summary: 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.
- Collaborate with technical staff to build quality assurance processes that identify anomalies and outliers in district- and state-level financial data.
- Contribute to developed processes to correctly incorporate contributions from sub-contractor matter experts.
- Document analytical methods, assumptions, and validation results clearly and reproducibly.
- Contribute to the 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 internal team members (data scientists, analysts) and external stakeholders (researchers, policy experts, state and district leaders) to advance the organizational mission.
Why This Role Matters
The data SPEND produces underpins billions of dollars in education funding and decades of research on resource allocation and system effectiveness. As a Machine Learning Engineer at SPEND, your work will directly contribute to preserving and improving a critical piece of the nation’s education data infrastructure.Qualifications
Meeting every preferred qualification is not required to advance in our search. Alignment with our mission, objective values, and technical impact is critical.Required
- Bachelor’s degree in Data Science, Computer Science, or a related field.
- Experience implementing and improving natural language processing (NLP) classification models.
- Experience leading machine learning workflows, including classification, feature engineering, and model evaluation.
- Strong working knowledge of Python, SQL, Google Cloud Platform, and collaborative coding practices.
- Experience translating analytical models and findings into clear, objective insights.
- Experience working with complex and inconsistently structured datasets.
- Ability to communicate analytical findings and machine learning model design clearly to both technical and non-technical audiences.
- Proficiency with statistical techniques (e.g., regressions, t-tests, confidence intervals).
- Availability: Must be available for core working hours of 10 AM – 5 PM ET to support national team collaboration.
- Travel: 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.
- Physical & Environmental: 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 the workday; ability to communicate and exchange information via video conference, phone, and email.
- Remote Work: Access to a quiet, professional remote work environment with a reliable, high-speed internet connection.
- Work Authorization: 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 for new team members at this time.
- Advanced degree (Master's or PhD).
- Experience, or demonstrated interest in, applying machine learning techniques to problems in public policy and government.
Compensation and Transparency
The salary range for this role is $150,000 to $180,000 annually. Because we are a fully remote team, geography is not a factor in salary; however, all team members must be U.S.-based.In modeling our commitment to openness and transparency, it’s important to note that starting salaries aren’t typically at or near the top of this range. This is to create opportunities for team members to earn raises throughout their tenure in the role.
ERS equivalent job tier for benefits purposes: Level IV
About our Fiscal Sponsor: Education Resource Strategies (ERS)
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.