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
Numbers & Facts
Location
Philadelphia, PA
Skills
Amazon Web Services (AWS)unmatched
Artificial Intelligence (AI)unmatched
Calibrationunmatched
Concreteunmatched
Data Analysisunmatched
Data Modelingunmatched
Data Qualityunmatched
Data Scienceunmatched
Decision Supportunmatched
Establish Prioritiesunmatched
Machine Learningunmatched
Microsoft Windows Azureunmatched
Model Validationunmatched
Performance Analysisunmatched
Performance Modelingunmatched
Performance Reviewsunmatched
Predictive Modelingunmatched
Production Supportunmatched
Prototypingunmatched
Python Programming/Scripting Languageunmatched
Requirements Validation/Verificationunmatched
Riskunmatched
SQL (Structured Query Language)unmatched
Software Engineeringunmatched
Structured Dataunmatched
Technical Leadershipunmatched
Testingunmatched
Training Data Setsunmatched
Validation Planunmatched
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