Principal Machine Learning Engineer

Delan Associates, Inc; Philadelphia (Philadelphia County); On-site; USD 150,000 - 210,000; Full time

Job summary

Delan Associates, Inc is seeking a Principal Machine Learning Engineer to lead hands-on ML initiatives, model development, and production readiness.

You will build, validate, deploy, and iterate models for scoring, risk detection, and decision support, while translating business problems into concrete ML designs.

Collaborate with stakeholders, maintain clean code and documentation, and guide senior technical direction with strong modeling judgment.

Qualifications

• 5+ years of hands-on machine learning model development experience.
• 3+ years 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.

Responsibilities

• 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.

Skills

Python

SQL

Spark

Databricks

MLflow

Scikit-learn

XGBoost

Stakeholder engagement

Production ML lifecycle

Tools

Databricks

Spark

MLflow

Snowflake

Azure

AWS

Job description

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