Applied Data Scientist
Hybrid San Francisco Full-time Mid-level Data Science
At a glance
Mid-level Data Science role at Hop. San Francisco · full-time · no salary stated · we estimate $172,375–$223,788.
Estimated pay
Hop has not published a salary for this role. The figure below is our estimate, not theirs.
$172,375 – $223,788
What the middle half of mid-level data science roles in San Francisco paid, among postings that did state a salary.
Worked out from 32 postings that stated pay, median $207,750. Last updated Oct 24, 2026.
RoleData Science
SeniorityMid-level
LocationSan Francisco
WorkplaceHybrid
EmploymentFull-time
PostedApr 22, 2026 · 6mo ago
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain models that classify, extract, detect trends and score predictions across structured and unstructured data
- Test model accuracy and bias with experiments and benchmarks, then check outputs at scale
- Use embeddings, named-entity recognition and text classification in practical data pipelines
- Work with engineers to launch models, monitor their performance and catch drift
- Create ways to evaluate AI-generated results across different product uses
What you bring
- A bachelor’s or master’s degree in statistics, computer science, applied mathematics or another quantitative field
- Three to five years in applied data science, including at least two years using NLP or large text datasets in production
- Strong Python skills, including pandas, scikit-learn and PyTorch or TensorFlow, plus working SQL proficiency
- A record of putting models into production and experience with enterprise-scale embeddings and semantic similarity
Who this fits
This mid-level role suits someone who has taken data science models from development into production and worked with NLP on large text datasets. You’ll work in San Francisco’s SoMa neighborhood, in the office at least four days a week. You must be authorized to work in the US without employer visa sponsorship, now or in the future.
From the employer
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.
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.
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