Senior Data Science Engineer

NetSPI® pioneered Penetration Testing as a Service (PTaaS) and leads the industry in modern pentesting. Combining world-class security professionals with AI and automation, NetSPI delivers clarity, speed, and scale across 50+ pentest types, attack surface management, and vulnerability prioritization. The NetSPI platform streamlines workflows and accelerates remediation, enabling our experts to focus on deep dive testing that uncovers vulnerabilities others miss. Trusted by the top 10 U.S. banks and Fortune 500 companies worldwide, NetSPI has been driving security innovation since 2001.
NetSPI is on an exciting growth journey as we disrupt and improve the proactive security market. We are looking for individuals with a collaborative, innovative, and customer-first mindset to join our team. Learn more about our award-winning workplace culture and get to know our A-Team at www.netspi.com/careers.
Join the mission as a Senior Data Science Engineer! 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.
Responsibilities
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.
Minimum Qualifications
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.
We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.

Work arrangement
Yes