About this role

Build and maintain ML models for classification, extraction, trend detection, and predictive scoring on large structured and unstructured datasets.
Design experiments and benchmarks to measure model accuracy, reduce bias, and validate outputs at scale.
Apply NLP techniques — embeddings, NER, text classification — to real-world data pipelines.
Partner with engineering to move models from experimentation to production; own monitoring and drift detection.
Build evaluation frameworks for AI-generated outputs across multiple product use cases.

BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field.
3–5 years of applied data science; minimum 2 years working with NLP or large-scale text data in production.
Strong Python (pandas, scikit-learn, PyTorch or TensorFlow); proficient in SQL.
Demonstrated track record of shipping models into production, not just producing analysis.
Experience with embedding models and semantic similarity at enterprise scale.
No visa sponsorship. Must be authorized to work in the US without current or future employer sponsorship.

Applied Data Scientist

Key skills