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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in the full model lifecycle, including data exploration, model training, validation, and deployment using Python and AWS SageMaker. Proficient in statistical analysis, exploratory data analysis, and technical writing to support model development and operational efficiency.
Highest-signal resume keywords
Model Lifecycle ManagementPython ProgrammingAWS SageMakerSQL ProficiencyCI/CD Experience
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Work across the full model lifecycle: data, training, validation, release, serving, monitoring, and iteration
- Explore and validate data before and after modeling, and document findings
- Run SageMaker training jobs and pipelines in Python
- Track experiments with MLflow and validate models against held-out cohorts
- Register, promote, and deploy model versions with defensible data, code, and model lineage
- Operate real-time endpoints and batch scoring using a feature store and Snowflake
- Tune latency, cost, and capacity
- Monitor prediction quality, failure rates, and drift through dashboards and alerts
- Conduct monthly production-health reviews and blameless postmortems
- Propose next model versions using production evidence and contribute to design RFCs
- Help analysts, product, and data teams understand and use model outputs
- Generalize models across subjects and curricula without forking code
- Re-score large student populations while live services continue operating
- Run model versions side by side and retire versions safely
- Improve training and serving efficiency without changing model behavior
- Work on a stream-aligned team alongside a senior or staff engineer
Requirements
What you’ll need- Track record of shipping models or ML systems that others depended on
- For IC4, typically three or more years of hands-on work, or a doctorate combined with relevant practical experience
- Master’s or PhD in a quantitative field, including psychometrics, is expected
- Idiomatic, typed, tested Python
- Experience with CI/CD, GitHub Actions, and Terraform-managed infrastructure
- Solid SQL
- Statistics foundation, including posterior, likelihood, and calibration plots
- Exploratory data analysis skills
- Excellent technical writing
- Working knowledge of AWS, ideally SageMaker
- Working knowledge of an experiment tracker such as MLflow
- Ability to assess whether work is ready to ship and support the assessment with evidence
- Helpful: psychometrics or education measurement, cognitive diagnostic models or Bayesian networks, probabilistic programming such as Stan or PyMC, R, Snowflake and dbt, Terraform, GitHub Actions, knowledge graphs or ontologies, and K–12 assessment data experience
Benefits
Comp & perks- Salary range of $110,000 to $115,000
- Culture and benefits program (specific benefits provided via the linked HMH page)
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Model TrainingData ValidationExploratory Data AnalysisStatistics FoundationExperiment TrackingModel DeploymentLatency TuningBatch ScoringMonitoring Prediction QualityTechnical Writing
Soft Skills
CollaborationCommunication
Tools & Technologies
AWSSageMakerMLflowSnowflakeGitHub ActionsTerraformDbt
Certifications & Qualifications
Master’s DegreePhD
Industry Keywords
PsychometricsEducation MeasurementCognitive Diagnostic ModelsBayesian NetworksProbabilistic ProgrammingK–12 Assessment Data