Principal Machine Learning Engineer

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.

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