Principal Machine Learning Engineer

Key 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.

Requirement Shaping and Stakeholder Partnership

• Work directly with business, product, analytics, operations, and engineering stakeholders to clarify what a model is intended to predict, explain, recommend, or trigger.
• Translate business questions into measurable ML objectives, target variables, features, validation approaches, and success metrics.
• Ask practical questions early: who will use the score, what action will it inform, what does a false positive or false negative mean, and how will we know the model is creating value?
• Communicate model behavior, tradeoffs, limitations, and recommended usage clearly to both technical and non-technical audiences.
• Help the team avoid becoming an AI ticket factory by shaping solutions, not just executing requests.

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 management, explainable AI, 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.