I care about what happens after the model predicts.
I’m a Data Scientist based in New York, working across applied machine learning, AI systems, retrieval, evaluation, causal inference, and data infrastructure.
My work has included public-sector analytics, RAG systems, AI quality and model evaluation, forecasting, nationwide demographic pipelines, and causal ML for intervention decisions.
Systems where evidence, uncertainty, and decisions all matter.
I like building systems where a model is not the end product. The interesting questions are often: what evidence should it see, how much confidence is enough, when should the workflow escalate, and how do we know the decision improved?
I also enjoy independent experiments where I can isolate one question and test it rigorously, like retrieval-unit design in multi-hop RAG or robustness in tabular foundation models.