AI Data Scientist II

About the role

The AI Data Scientist II in AI, Data Science and Machine Learning will execute defined data science, experimentation, and model evaluation work for systems that interpret and use aerial imagery. Working with AI and data science, engineering, and product partners, this role will develop and apply metrics, test methods, analysis workflows, and evaluation tools that support product objectives. The role will solve technical problems limited in scope, make evidence-based judgments and proposals, and complete task-level deliverables with minimal coaching. We are a fast-paced, collaborative team driven by continuous improvement. We are looking for a motivated and organized emerging professional who learns new technologies and development lifecycle practices, communicates effectively across team boundaries, and documents work so that others can understand and build on it. This is a full-time, remote role, with a base salary of $98,000 - $134,000, bonus eligible.

Responsibilities

• Model and Agentic System Evaluation: Execute benchmarking tasks for machine learning and agentic AI systems using defined measures such as task success, accuracy, robustness, and common failure patterns. Apply and help maintain established evaluation methods for computer vision and machine learning models, including detection, segmentation, and classification models. Develop and maintain quantitative metrics, test harness components, and curated evaluation datasets for realistic, production-representative scenarios. Analyze model outputs, errors, and test results to identify performance issues and propose practical improvements. Conduct defined comparisons across model architectures, prompt strategies, tools, and inference configurations, then summarize results and recommendations.
• Evaluation and Analysis: Execute defined offline evaluations, including model comparisons, stress tests, and edge-case testing. Perform structured error analysis and root-cause investigation across data, model, and system failures, escalating broader issues when appropriate. Document code, data, configurations, and evaluation results so that work can be repeated, reviewed, and improved by others. Compare model accuracy, reliability, processing time, and resource use, and communicate findings clearly to stakeholders.
• Cross-Functional Collaboration and Communication: Work with AI engineers, product managers, and research partners to translate evaluation results into clear