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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in model lifecycle management, including training, validation, and deployment using Python and AWS SageMaker. Proficient in data analysis, model monitoring, and collaboration with cross-functional teams to enhance model performance and reliability.
Highest-signal resume keywords
Model Lifecycle ManagementAWS SageMakerPython ProgrammingSQL ProficiencyExploratory Data Analysis
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Work across the full model lifecycle: data, training, validation, release, serving, monitoring, and subsequent versions
- Explore and check data before and after model fitting, and document findings
- Train and validate models using SageMaker training jobs and pipelines in Python
- Track experiments in MLflow and validate against held-out cohorts before promotion
- Register, promote, and deploy model versions with defensible data, code, weights, and serving lineage
- Release models through code review and CI
- Serve models through 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
- Use production evidence to propose model improvements and shape team RFCs
- Help analysts, product, and data teams understand model outputs and use them effectively
- Collaborate on stream-aligned teams with senior or staff engineers
- Build models that generalize across subjects or curricula without forking code
- Re-score large student populations while live services continue serving
- Run model versions side by side and retire versions safely
- Improve training and serving efficiency without changing model outputs
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, psychometrics included, expected
- Idiomatic, typed, tested Python
- Comfort 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; K–12 assessment data experience
- Must be eligible to work in the United States; HMH participates in E-Verify
Benefits
Comp & perks- Salary range of $110,000 to $115,000
- Equal Employment Opportunity workplace commitment
- Work environment free from discrimination and harassment
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 TrainingModel ValidationStatistical AnalysisCI/CD PracticesExperiment TrackingData MonitoringFeature Store UsageBatch ScoringLatency TuningTechnical Writing
Soft Skills
CollaborationCommunication
Tools & Technologies
MLflowSnowflakeGitHub ActionsTerraform
Industry Keywords
PsychometricsCognitive Diagnostic ModelsBayesian NetworksK–12 Assessment Data