AI/ML Data Scientist – Predictive Modeling

Position Overview We are seeking an experienced ML/AI Data Scientist to design, build, train, evaluate, and deploy predictive models that solve complex business and operational problems. The ideal candidate combines strong statistical foundations with hands-on experience in data analysis, feature engineering, model development, and performance evaluation. This onsite role is based in Dallas, Texas.

Key Responsibilities - Translate business problems into well-defined machine learning and predictive modeling objectives. - Collect, clean, transform, and analyze structured and unstructured data from multiple sources. - Perform exploratory data analysis to identify trends, relationships, anomalies, biases, and data-quality issues. - Develop predictive models from scratch, including data preparation, feature engineering, training, validation, testing, and optimization. - Build and apply regression models for forecasting, estimation, risk scoring, pricing, demand prediction, and related use cases. - Build and apply classification models for segmentation, fraud detection, churn prediction, recommendation, anomaly detection, and other decision-support applications. - Select appropriate algorithms based on the problem type, data characteristics, business requirements, interpretability needs, and operational constraints. - Compare baseline, linear, tree-based, ensemble, and other appropriate modeling approaches. - Tune model hyperparameters and use appropriate cross-validation strategies to improve generalization. - Experience building and

AI/ML Data Scientist – Predictive Modeling

Position Overview We are seeking an experienced ML/AI Data Scientist to design, build, train, evaluate, and deploy predictive models that solve complex business and operational problems. The ideal candidate combines strong statistical foundations with hands-on experience in data analysis, feature engineering, model development, and performance evaluation. This onsite role is based in Dallas, Texas.

Key Responsibilities - Translate business problems into well-defined machine learning and predictive modeling objectives. - Collect, clean, transform, and analyze structured and unstructured data from multiple sources. - Perform exploratory data analysis to identify trends, relationships, anomalies, biases, and data-quality issues. - Develop predictive models from scratch, including data preparation, feature engineering, training, validation, testing, and optimization. - Build and apply regression models for forecasting, estimation, risk scoring, pricing, demand prediction, and related use cases. - Build and apply classification models for segmentation, fraud detection, churn prediction, recommendation, anomaly detection, and other decision-support applications. - Select appropriate algorithms based on the problem type, data characteristics, business requirements, interpretability needs, and operational constraints. - Compare baseline, linear, tree-based, ensemble, and other appropriate modeling approaches. - Tune model hyperparameters and use appropriate cross-validation strategies to improve generalization. - Experience building and