Senior Data Scientist - Computer Vision
New
Z
Zesty.aiPropertyTech, InsurTech
Remote, CanadaFull-TimeSenior
SalaryMarket-competitive compensation and equity incentives
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Job Details
- Experience
- At least 5 years of experience applying machine learning techniques to computer vision applications.
- Required Skills
- DockerPythonSQLKubernetesMachine LearningPyTorchTensorflowBigQueryDeep LearningComputer Vision
Requirements
- At least 5 years of experience applying machine learning techniques to computer vision applications.
- BA/BS/BEng degree in Math, Physics, Computer Science, Engineering, Economics, or other related fields.
- Strong understanding of image processing, machine learning, deep learning, and AI principles.
- Extensive hands-on experience designing and implementing deep learning models.
- Experience deploying deep learning models in production environments.
- Proficiency in Python, with experience using deep learning frameworks (e.g., PyTorch, TensorFlow) and related libraries (e.g., OpenCV, Pillow, Matplotlib, Plotly).
- Understanding of data preprocessing and augmentation techniques for large-scale image datasets.
- Experience working with cloud computing platforms (e.g., Google Cloud Platform, AWS) and containerization/orchestration tools (e.g., Kubernetes, Docker).
- Familiarity with version control systems (e.g., Git) and collaborative development workflows.
- Experience working with BigQuery and SQL.
- Experience using generative AI coding tools such as Claude Code and Cursor.
- Proven ability to mentor and guide junior team members.
Responsibilities
- Explore data sources and develop new PropertyTech and InsurTech models using data science, including machine learning and deep learning.
- Research, design, and implement deep learning algorithms and architectures that extract insights from imagery sources such as aerial imagery and geospatial data.
- Lead the development of high-quality datasets for robust model training and evaluation.
- Collaborate closely with cross-functional teams to understand requirements and translate product, engineering, and business constraints and questions into actionable data science problems.
- Coach and provide feedback to members of the Data Science and Machine Learning team, fostering skill development and knowledge sharing.
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