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# Senior Data Scientist

- **Company:** Zonos Inc
- **Location:** St. George, UT, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $275,000.0
- **Contract:** Permanent contract
- **Skills:** Software Debugging, Python (Programming Language), Machine Learning, Performance Tuning, SQL Databases, Graphics Processing Unit (GPU), Large Language Models, Deep Learning, Document Classification
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0b482172d121362e

## About the Role

+ 5+ years of hands-on data science work with a Bachelor's degree, or 3+ years with a Master's or PhD, including things you built that ran in production.
 + Real depth in statistics: inference, experimental design, and uncertainty. You understand what the numbers mean, not just how to produce them.
 + Practical experience with ML and LLMs/NLP, including fine-tuning, embeddings, evaluation, and text classification at scale.
 + Strong Python and SQL.
 * Nice to have: optimization or operations research, causal inference, forecasting, Bayesian methods, serving models on GPUs, and experience with trade, customs, logistics, ecommerce, or payments data.

## Description

Behind almost every part of that is a data problem. Inferring an HS code from a messy product title, estimating customs value, deciding when an answer is confident enough to guarantee, spotting the shipments that need a human, and pricing risk across 200+ countries all have real financial and regulatory consequences.

We're hiring a data scientist who solves problems and ships the solution. Sometimes that's a fine-tuned LLM. Sometimes it's a Bayesian model, a well-designed experiment, an optimization, a simulation, or a 30-line rule that beats everything else. We want someone with the whole toolbelt and the judgment to pick the right tool. Cleverness for its own sake doesn't interest us.

You'll report directly to our Chief Data Scientist and work side by side with engineering, taking problems from "fuzzy and important" to something running in production that customers rely on., * Own data problems at the heart of the product, such as classification, valuation, risk, and compliance. Choose the approach, whether that's statistical modeling, classical ML, deep learning and LLMs, optimization, or a simple rule.

 * Work with messy, multilingual product and shipment data. Figure out what it actually says before modeling anything.
 * Quantify uncertainty and make it useful. Decide when we're confident enough to act, when to route work to a human, and what each error costs.
 * Design the evaluations, experiments, and analyses that tell us whether something actually worked, and hold the line when it didn't.
 * Write production-quality Python and SQL, then ship, monitor, and iterate on what you build.
 * Join the on-call rotation and debug in production, including the subtle failures that only show up on real traffic.
 * Bring rigor to product and business decisions beyond your own projects.