[Hiring] Machine Learning Engineer @Education Resource Strategies

Role Description

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.

Key Responsibilities

• Innovation & Automation (Building the New System) – 80% of time
  • Focus: 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.
• System Stability & Continuity (Strengthening the Current System) – 20% of time
  • 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.
• Cross-Cutting Contributions
  • 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.

Qualifications

• 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).

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 the workday; ability to communicate and exchange information via video conference, phone, and email.
• 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 for new team members at this time.

Benefits

• 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.
• Starting salaries aren’t typically at or near the top of this range to create opportunities for team members to earn raises throughout their tenure in the role.
• ERS equivalent job tier for benefits purposes: Level IV.