Applied Data Scientist

🏢 Hop · 📍 San Francisco, CA · 💼 hybrid · ⏰ Full- Time
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Position Summary & Responsibilities

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.
🎯 Candidate Preparation Guide For Hop Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Assesses modern HR agility, employee lifecycle management, and the ability to balance empathetic people operations with business growth goals.

📋 Preparation Priorities (Key Areas to Clarify):
  • 1. Adopting a purely bureaucratic or rigid, policy-first approach to human resources
  • 2. Failing to maintain strict confidentiality standards when discussing past scenarios
  • 3. Showing lack of adaptability to fast-changing startup or scale-up environments
✨ Recommended Role Alignment Hook:

I design scalable people operations that reduce friction for managers and deeply protect company culture.

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