EI
Principal Machine Learning Engineer
Expert In Recruitment Solutions
🇺🇸 United States
On-site
Staff / Principal
2 months ago
- Machine Learning
- Python
- SQL
- Databricks
- MLflow
- scikit-learn
- XGBoost
- Snowflake
- Azure
- AWS
- calibration
- MLOps
- AI
2 months ago
Hands-On Model Development
- Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
- Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.
- Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.
- Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.
- Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.
- Move quickly from data exploration to prototype to validated model to production-ready capability.
Required Qualifications
- Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.
- 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.
- 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
- Strong hands-on experience with Python and SQL.
- Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
- Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
- Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.
- Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
- Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.
- Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.
Scoring, Scorecards, and Transparent Models
Production ML and MLOps
Product and Rapid-Build Execution
Generative AI and AI Automation
Requirement Shaping and Stakeholder Partnership
Principal Machine Learning Engineer · Expert In Recruitment Solutions