Applied Data Scientist
This role focuses on advancing AI research for workforce intelligence by developing high-impact ML models and optimizing production pipelines for Fortune 100 clients. You’ll collaborate closely with engineering to deploy scalable solutions while ensuring model fairness and accuracy at enterprise scale.
Skills required
About this role
Draup is a Series A-funded agentic AI company building the intelligence layer for how global enterprises make workforce and go-to-market decisions. We work with 250+ enterprise clients — including 5 of the Fortune 10 — processing 1B+ job descriptions, 850M+ professional profiles, and signals from 100+ labor databases.
We are now building our Silicon Valley engineering team — a small, senior group focused on next-generation AI research and product.
Location: San Francisco, SoMa — minimum 4 days per week in-office.
What you'll do
• 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.
What we require
• 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.
