Principal Machine Learning Engineer

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning predictive modeling scoring decisioning and applied AI initiatives. This role will primarily focus on building validating deploying and improving machine learning models while also bringing principal-level judgment to problem definition model design stakeholder engagement and production readiness.

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