Data Scientist (Sovereign Territory Surveillance)

SRT Marine Systems plc; Walham; Híbrido; GBP 63.000 - 103.000; Jornada completa

Descripción de la vacante

SRT Marine Systems plc in the United Kingdom is seeking a Data Scientist to solve maritime and security challenges using time series and image/video data. You will design and evaluate ML models for behavior classification, anomaly detection, and track patterns in geospatial data, integrating outputs into user-facing features.

Collaborate with infra/edge engineers to build scalable data pipelines for AIS and vessel video, contribute to feature development such as alerting and visualization, and

Formación

• Essential: Solve maritime and security-based problems such as detection and classification for time series and image/video data streams.
• Essential: Develop and evaluate ML models for behaviour classification, anomaly detection, and track pattern recognition within geospatial data.
• Essential: Research, prototype, extend, and optimise suitable vision and time-series models for these problems.
• Essential: Collaborate with developers to integrate model outputs into user-facing features.
• Essential: Work with infrastructure and edge-based engineers to build scalable pipelines for ingesting, processing, annotating maritime data (AIS and vessel video).
• Essential: Contribute to feature development in areas such as alert generation and visualisation.
• Essential: Document model performance, testing procedures, and experiment outcomes, tracking datasets and model versions.

Responsabilidades

• Solve maritime and security-based problems such as detection and classification for time series and image/video data streams.
• Develop and evaluate ML models for behaviour classification, anomaly detection, and track pattern recognition within geospatial data.
• Research, prototype, extend, and optimise vision and time-series models.
• Collaborate with developers to integrate model outputs into user-facing features.
• Work with infrastructure and edge-based engineers to build scalable pipelines for ingesting and processing maritime data (AIS and vessel video).
• Contribute to feature development in areas such as alert generation and visualisation.
• Document model performance, testing procedures, and experiment outcomes, while tracking datasets and model versions.

Conocimientos

Time series analysis

Image/video data

ML model development

Geospatial data

Vessel AIS data

Edge pipelines

Feature visualization

Experiment tracking

Documentation

Herramientas

Docker

AWS ECS/ECR

Neo4J

MLflow

Weights & Biases

Ray

CI/CD

Descripción del empleo

Salary: £63,000 - 103,000 per year

Requirements:

• Essential:
• Solve maritime and security-based problems such as detection and classification for time series and image/video data streams.
• Develop and evaluate ML models for behaviour classification, anomaly detection, and track pattern recognition within geospatial data.
• Research, prototype, extend, and optimise suitable vision and time-series models for these problems.
• Collaborate with developers to integrate model outputs into user-facing features.
• Work with infrastructure and edge-based engineers within our company to build scalable pipelines for ingesting, processing, annotating, and managing large volumes of maritime data (e.g. AIS and vessel video footage).
• Contribute to feature development in areas such as alert generation and visualisation.
• Methodically apply best practices to document model performance, testing procedures, and experiment outcomes, while tracking datasets and model versions.
• Desirable:
• Experience fine-tuning, extending, and evaluating modern video or radar-based detection and tracking models, including appropriate performance metrics.
• Experience creating, managing, and interrogating graph databases (e.g., Neo4j).
• Experience with maritime, aerial, or other remote-sensing imagery.
• Exposure to MLOps tools (MLflow, Weights & Biases, Ray etc.).
• Experience deploying systems with containerisation, both on premises and in the cloud (e.g. using Docker and AWS ECS/ECR), and their ongoing maintenance/support.
• Experience of CI/CD within a machine learning / data science environment.

Responsibilities:

• Solve maritime and security-based problems such as detection and classification for time series and image/video data streams.
• Develop and evaluate ML models for behaviour classification, anomaly detection, and track pattern recognition within geospatial data.
• Research, prototype, extend, and optimise suitable vision and time-series models for these problems.
• Collaborate with developers to integrate model outputs into user-facing features.
• Work with infrastructure and edge-based engineers within our company to build scalable pipelines for ingesting, processing, annotating, and managing large volumes of maritime data (e.g. AIS and vessel video footage).
• Contribute to feature development in areas such as alert generation and visualisation.
• Methodically apply best practices to document model performance, testing procedures, and experiment outcomes, while tracking datasets and model versions.

Technologies:

• AWS
• CI/CD
• Cloud
• Docker
• Fine-tuning
• Support
• Machine Learning
• MLflow
• MLOps
• Neo4J
• PLC
• Security

More:

We are SRT Marine Systems plc, a market leader in international marine surveillance technology and systems, headquartered in the UK with a global customer base. We are expanding into Land Border Surveillance Systems through our Sovereign Territory Surveillance (STS) systems, which support civil defence security and the monitoring of sovereign land borders. The Data Scientist role is based in our Bristol office and aligned with our hybrid working policy. You will join a data-orientated team focused on research and delivery, where collaboration, open communication, and timely feedback are valued. We offer a highly competitive salary and benefits package, 25 days annual leave rising to 28 days with service, and real individual development opportunities. We are an equal opportunity employer committed to an inclusive environment for all employees.

last updated 38 week of 2026