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# Senior Data Scientist

- **Company:** NetSPI, LLC
- **Location:** Minneapolis, MN, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software System Penetration Testing, Data Visualization, Software Design Documents, Python (Programming Language), Machine Learning, SQL Databases, Web Applications, Feature Engineering, Large Language Models, Data Layers, Information Technology
- **Published:** September 21, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/plrefj2qi9

## About the Role

* Master's or PhD in Data Science, Statistics, Computer Science, or a related field.
 * Minimum 7 years of experience in data science, with a strong background in machine learning and statistical analysis.
 * Experience with training and fine tuning LLMs is heavily preferred.
 * Expertise in Python, R, or other data science languages and tools.
 * Strong experience with data visualization and communication of complex results.
 * Excellent problem-solving abilities and a strong focus on delivering actionable insights.
 * Exceptional communication skills, capable of conveying complex data concepts to a non-technical audience.

## Description

Join the mission as a Senior Data Scientist! You will work on the core of our AI pentesting product - a large, Pythonic system that maps web applications, reasons about them with LLM agents, and confirms real vulnerabilities with deterministic evidence. This is an engineering and research role - the work is designing innovative systems and original research. We are looking for a strong developer who is equally at home in statistical modelling, classical ML, and the design of agentic systems - someone who can pick up an existing architecture, understand why it was built that way, and extend it., Build and Extend the Product

 * Own features end to end in a large Python codebase - design, implementation, tests, and the design documents that govern them.
 * Work fluently across the data layer (SQL schema design, query-heavy analysis pipelines) and the agent layer that consumes it.

Agentic System Design

 * Design, evaluate, and harden LLM-driven agents: prompt and tool design, deterministic confirmation of model output, evidence gating, and reproducible behaviour from a fixed input snapshot.
 * Use LLMs as a development instrument as well as a product component - specification-driven planning, agent-assisted implementation with comprehensive and robust review practices.

Statistical and ML Rigour

 * Build and validate models under scarce, noisy ground truth: calibration, uncertainty quantification, leakage detection, temporal and grouped validation, drift monitoring.
 * Apply the full traditional toolkit where it is the right answer - gradient-boosted trees, graph features and embeddings, feature engineering, signal-versus-background analysis, ML. Justify all choices on measured performance.
 * Define what "better" means for each system: the metric, the operating point, and the failure mode we are willing to accept.

Research Judgement

 * Take open problems from written roadmaps to working systems.
 * Reduce requirements to a testable design. Document decisions.

Collaboration

 * Partner with pentesters and platform engineers to turn practitioner expertise into product behaviour.
 * Communicate design and results to technical and non-technical audiences, including leadership.


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