senior-data-scientist

Statistical modeling for A/B tests involves designing experiments, processing resulting data, and analyzing feature effects. This capability supports causal inference to evaluate impacts on user engagement and drive informed business decisions.

Building scalable data pipelines for production ML requires feature engineering and model deployment. This capability handles end-to-end data processing to create production-grade AI systems and drive data-driven decisions.

Yes, Python, R, and SQL are supported for statistical modeling and machine learning tasks. These languages integrate with various ML frameworks to handle experiment design, feature engineering, model evaluation, and deployment.

Evaluating predictive models involves using statistical modeling and experimentation features. This capability supports model evaluation to ensure production-grade AI/ML systems meet analytical requirements before deployment.

Causal inference is needed when understanding the direct effect of a feature on outcomes like user engagement. Standard statistical modeling identifies correlations, while causal inference determines actual impact through designed experiments.