Data Scientist II

The Data Scientist II will contribute to the development and improvement of machine learning and deep learning models that interpret high-resolution aerial imagery and solve complex real-world problems. You will work across the model development lifecycle—from data exploration and experimentation to model training, evaluation, and performance analysis. Working with senior data scientists and cross-functional partners, you will develop solutions to well-defined problems and execute project deliverables with minimal coaching. This role will collaborate closely with data scientists, machine learning engineers, product teams, and domain experts to translate business problems into machine learning solutions. The successful candidate will demonstrate strong analytical and problem-solving skills, sound judgment within their area of expertise, and the ability to clearly communicate technical concepts and findings across teams.

We are a fast-paced, energetic team driven by continuous process improvement. We’re looking for motivated, organized, and independent team members who are passionate about solving challenging problems using data, machine learning, and computer vision. This position requires strong analytical and programming skills, curiosity to explore new technologies, and the ability to quickly learn and apply evolving AI techniques.

• Develop, train, evaluate, and improve machine learning and deep learning models using aerial imagery and geospatial datasets.
• Design and conduct experiments to evaluate model architectures, training strategies, datasets, and other approaches to improve model performance.
• Perform data exploration, preprocessing, and error analysis to identify data quality issues, model limitations, and opportunities for improvement.
• Develop computer vision solutions for tasks such as segmentation, depth estimation, object detection, and image classification.
• Apply established machine learning practices and technical judgment to solve defined problems with minimal guidance.
• Collaborate with data scientists, engineers, product teams, and domain experts to develop effective machine learning solutions.
• Contribute to reproducible training, evaluation, benchmarking, and model development workflows.
• Communicate findings and technical decisions, contribute to documentation and knowledge sharing, and participate in technical reviews and design discussions.

Required Experience

• 2-4 years of hands-on experience in data science, machine learning, computer vision, or a related field.
• Strong programming experience in Python, with experience using common data science and machine learning libraries.
• Hands-on experience developing and training deep learning models using frameworks such as PyTorch, TensorFlow, or similar.
• Understanding of fundamental machine learning concepts, including supervised learning, self-supervised learning, model evaluation, feature engineering and optimization.
• Familiarity with computer vision concepts and techniques, such as instance and semantic segmentation, depth estimation, object detection, image classification, or related tasks.
• Experience working with large datasets, including data preprocessing, dataset preparation, and exploratory data analysis.
• Strong analytical and problem-solving skills with the ability to investigate complex model and data-related issues and develop appropriate solutions within a defined problem space.
• Ability to design and execute experiments, interpret results, and clearly communicate findings and recommendations.
• Familiarity with cloud platforms such as AWS and GPU-based model training environments.
• Effective written and verbal communication skills and the ability to collaborate with cross-functional teams.
• Familiarity with version control systems, such as Git, and collaborative software development practices.

Preferred Experience

• Experience working with aerial, satellite, geospatial, or other large-scale imagery datasets.
• Experience with modern deep learning architectures such as Vision Transformers, Foundation Models and CNNs.
• Exposure to MLOps practices, including experiment tracking, model versioning, reproducible training pipelines, and model lifecycle management.
• Experience with distributed or large-scale model training.
• Familiarity with geospatial data formats, GIS concepts, or remote sensing applications.

Core Competencies

The successful candidate will demonstrate strength in the following competencies as well as which can be found here:

• Drive & Follow Through - Takes initiative and turns ideas into action.
• Adaptability in Uncertainty - Adjusts quickly and performs through change.
• Customer-Centered Mindset - Puts customer needs at the center of decisions.
• Judgment & Problem Solving - Makes sound decisions and solves problems effectively.
• Role-Specific Expertise - Applies strong functional expertise to deliver results.
• Results Accountability - Owns outcomes and delivers on commitments.
• Work Prioritization & Execution - Focuses on priorities and delivers on time.