Senior Data Scientist

Senior Data Scientist

Our Client, a leader in geospatial intelligence, is seeking a highly skilled Senior Data Scientist to join their innovative team. Operating within the cutting-edge field of AI and machine learning, the organisation focuses on developing sophisticated spatial intelligence solutions derived from high-resolution aerial imagery. Known for a collaborative and forward-thinking culture, Our Client prides itself on fostering innovation, continuous professional growth, and delivering impactful technologies that revolutionise environmental monitoring, disaster response, urban planning, and infrastructure management.

This position offers a unique opportunity to shape the future of agentic AI systems, contributing to the evaluation and optimisation of complex models used in high-stakes, real-world applications. The role plays a pivotal part in driving the organisation's strategic objectives by ensuring the robustness, efficiency, and reliability of its AI solutions. If you thrive in a fast-paced environment where your expertise can influence meaningful technological advancements, this opportunity is perfect for you.

Role Summary

This role has been created to strengthen Our Client's capabilities in evaluating and benchmarking advanced AI systems, particularly focusing on agentic and computer vision models that operate on aerial imagery. As a Senior Data Scientist specialising in benchmarking, you will be at the forefront of establishing and refining evaluation protocols that ensure the integrity and performance of AI models as they are deployed into production environments. Your work will directly impact the scalability and trustworthiness of the AI driven solutions that serve diverse sectors such as urban development, environmental science, and government.

By contributing to key assessment methodologies, spearheading experimental design, and collaborating across multidisciplinary teams, you will help shape the organisation's approach to responsible AI deployment, setting new standards for model assessment in a rapidly evolving industry.

Responsibilities

• Develop and implement comprehensive benchmarking frameworks to evaluate the performance, robustness, and latency of AI models in real-world scenarios.


• Design and conduct rigorous experiments, including A/B testing and edge-case analyses, to identify model strengths and weaknesses.


• Generate quantitative metrics and maintain gold-standard datasets to support ongoing model assessment activities.


• Analyse agent decision-making processes, tool usage, and failure modes to recommend improvements and optimise system performance.


• Collaborate with engineering, data science, and product teams to translate evaluation outcomes into actionable insights and deployment strategies.


• Build and maintain automated systems for continuous evaluation and monitoring of AI models, ensuring real-time performance tracking.


• Stay informed of advancements in AI benchmarking standards and industry best practices, integrating new protocols as relevant.


• Advocate for responsible AI practices, including fairness, bias mitigation, and interpretability, within evaluation workflows.


• Document and communicate evaluation results clearly for cross-team clarity and strategic decision-making.

Essential Skills & Experience

• Master's, or PhD in Computer Science, Data Science, Artificial Intelligence, or a related technical discipline.


• At least 5 years of experience evaluating and benchmarking machine learning models within research or production settings.


• Proven expertise in computer vision model evaluation, including metrics for detection, segmentation, and classification.


• Experience assessing complex agentic systems, multi-step workflows, or large language model pipelines.


• Strong foundation in experimental design, statistical analysis, and hypothesis testing.


• Advanced proficiency in Python and relevant ML/data science libraries such as PyTorch, NumPy, Pandas, and scikit-learn.


• Familiarity with experiment tracking, version control, and reproducibility best practices.


• Experience working with cloud-based environments and containerised workflows.

Desirable Skills & Experience

• Hands-on experience with geospatial datasets, aerial imagery, and domain-specific evaluation challenges.


• Ability to design synthetic or simulated benchmark datasets for specialised testing scenarios.


• Knowledge of performance monitoring, data drift detection, and regression analysis.


• Contributions to open-source benchmarking tools or relevant academic publications.


• Proven ability to develop internal evaluation platforms or automate benchmarking infrastructure.

We invite qualified candidates with the relevant experience to submit their CV for consideration. If you are passionate about advancing AI evaluation standards and eager to make an impact on innovative spatial intelligence solutions, we look forward to hearing from you.