Senior Machine Learning Engineer (ML Modeling, NLP, Python)

Responsibilities: Design, develop, evaluate, and implement machine learning classifiers for content classification and segmentation use cases. Explore, analyze, and prepare content datasets using Python, pandas, SQL, Spark, and related data science tools. Develop features from structured and semi-structured metadata, including categorical, numerical, text-derived, and aggregated features. Build robust feature-processing workflows, including missing-value handling, categorical encoding, scaling, normalization, feature selection, and other appropriate transformations. Develop multi-class and multi-label classification models using appropriate supervised machine learning techniques, and train, tune, and compare models using cross- validation, and hyperparameter optimization, Partner with data scientists, engineers, and content stakeholders to translate requirements into practical machine learning solutions. Apply strong software development practices, including modular code, version control, testing, logging, code review, and documentation.

Basic Qualifications: 5+ years of industry experience building and deploying machine learning models in production environments. Strong proficiency in Python, with hands-on experience using pandas and scikit-learn for data preparation and machine learning. Strong understanding of supervised machine learning and classification techniques, including linear models, decision trees, random forests, ensemble methods, and gradient-boosted models. Strong experience with feature engineering, feature preprocessing, and feature selection for structured and semi-structured datasets. Experience developing multi-class and/or multi-label classification models. Strong SQL skills and experience querying, joining, aggregating, and analyzing large datasets. Ability to independently move from exploratory data analysis and feature development through model training, evaluation, and implementation. Strong analytical and problem-solving skills, with the ability to make pragmatic modeling decisions based on data, project requirements, and delivery constraints. Strong written and verbal communication skills, with the ability to clearly explain technical approaches and results to technical and non-technical stakeholders. Ability to operate effectively in a fast-paced environment, ramp up quickly on existing data and code, and independently deliver within a project-based contract engagement.