Applied Data Scientist

HopHR; San Francisco (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

HopHR is seeking an experienced Data Scientist to join our engineering team in San Francisco. The role involves building and maintaining machine learning models, applying NLP techniques to large datasets, and collaborating with engineering teams to transition models from experimentation to production. Applicants should have a BS/MS in a quantitative field and 3-5 years of experience in applied data science, particularly with NLP and production-level models. This is a hybrid position requiring at least 3 days in-office per week.

Qualifications

• 3-5 years of applied data science, including 2 years with NLP or large-scale text data.
• Demonstrated track record of deploying models into production.
• Experience with embedding models and semantic similarity.

Responsibilities

• Build and maintain ML models for classification, extraction, and trend detection.
• Design experiments and benchmarks to measure model accuracy.
• Apply NLP techniques to real-world data pipelines.

Skills

Python

SQL

Machine Learning

Natural Language Processing

Statistics

Education

BS/MS in Statistics, Computer Science, Applied Mathematics, or a quantitative field

Tools

pandas

scikit-learn

PyTorch

TensorFlow

Job description

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 — 450 Townsend St. Hybrid: minimum 3 days in-office per week, 2 days flexible.

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.