Principal Machine Learning Engineer

Job Description

Job Description

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.

• 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.