Data Scientist

About the role

Role: Data Scientist Contract: Permanent Salary: Very Competitive Location: Oxford / London Security clearance: This role requires eligibility for UK security clearance (BPSS and SC). SC requires 5 years of continuous UK residency. An active SC or DV clearance is a strong advantage. If you are not currently eligible, we will not be able to progress with your application. A note from the Founders Oxford Dynamics is at an inflection point. We operate in some of the most complex and high-stakes environments in the world defence - national security, AI and robotics. The decisions we make now will define not just how fast we grow, but who we become. You will work closely with the whole team. You will be trusted with judgment calls. You will influence the business. And you will see the impact of your work every day. If you are excited by ownership, pace and purpose, and by building something that genuinely matters - we would love to hear from you. Core Remit You turn messy, real-world sensor and signals data into detection capability that tells an operator what matters - whether that calls for a machine-learning model, a deterministic rule, or a technique no one has tried yet - and you are honest about what the data can and cannot say. Your Brief - Build detection capability on real, hard data: electronic intelligence (signals from emitters), maritime vessel-tracking data, and fused multi-source feeds. The kind of data that arrives with gaps, errors, and deliberate manipulation. - Pick the right technique for the problem - not the fashionable one. Sometimes that's a machine-learning model; often it's a deterministic rule, a statistical test, or a physics- or behaviour-based heuristic; sometimes it's something you invent. We care that it's correct, explainable, and defensible, not that it counts as "AI". Novel thinking and new techniques are actively encouraged. - Engineer the features and signals that make or break detection - the per-event aggregates, rolling-window statistics, and geospatial and temporal patterns that determine whether a rare anomaly is caught or missed. - Define what "good" looks like for a given intelligence type (an INT) and the threat it serves, then design the techniques that enforce that quality and detect and control drift over time - so a model or rule that was right last month doesn't quietly rot as the world changes. - Own the pipelines that get detection from idea to production fast. The goal is to ship and update models and rules at a rate of change that keeps pace with agentic workflows - continuous, automated, and safe, not a release every quarter. - Own evaluation end to end: design the holdout, read the precision-recall trade-off, calibrate thresholds against how many false alarms an operator can actually tolerate, and say plainly where the signal is real and where it is noise. - Take exploratory analysis and harden it into scored, reproducible capability that operators and our platform can rely on - moving it from "interesting notebook" to "trusted detection". - Ship through our MLOps platform so everything is reproducible, versioned, and auditable - not one-off scripts on your laptop. Someone else should be able to retrain and re-serve what you build. - Work alongside our engineers and the analysts who understand the operational domain, and translate between the data and the mission - explaining to non-specialists what a technique can and cannot tell them. The Right Fit - You are an applied data scientist, not a theory specialist. You have built and shipped detection on real datasets - not just toy or competition data, and you know the difference. - You don't reach for ML by reflex. You're as comfortable with a deterministic rule, a statistical method, or a behaviour-based heuristic as with a model, and you choose the technique that actually fits the problem. The best engineers here invent the technique when none of the obvious ones work. - You are fluent in Python and the scientific stack (NumPy, pandas, scikit-learn) , and comfortable with at least one deep-learning framework (TensorFlow or PyTorch) when the problem warrants it. - You have done anomaly detection, time-series, or geospatial work and have the scars to prove it - missing data, drift, imbalanced classes, and metrics that lie if you let them. - You think about quality and drift, not just accuracy on day one. You know how to define what "good" means for a detection problem and how to keep it good as the data and the threat move underneath you. - You are rigorous about evaluation. You can design a holdout that doesn't leak, resist overfitting and metric-gaming, and you would rather report an honest 70% than a flattering-but-fake 95%. - You write code others can build on. Version control, code review, reproducible pipelines - your work is not trapped in a notebook only you can run. - You can own the pipeline from the source platform, not just the model. You have worked hands-on with the upstream geospatial and data-

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at Oxford Dynamics? Share your experience