Lead Data Scientist - Geospatial AI/ML
New
IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- DockerPythonAgileArtificial IntelligenceJavaKubernetesMachine LearningC++ScalaMLOpsComputer Vision
Requirements
- Proven experience designing, building, testing, and deploying machine learning and AI solutions in production environments
- Strong understanding of machine learning algorithms
- Strong understanding of computer vision
- Strong understanding of geometry and 3D mathematics
- Strong understanding of point cloud processing
- Experience working in cloud-based distributed systems
- Experience working in Agile development environments
- Exposure to the full software development lifecycle
- Hands-on experience deploying models using Docker
- Hands-on experience deploying models using Kubernetes in production environments
- Proficiency in Python
- Proficiency in Java/Scala
- C++ experience is a strong advantage
- Strong problem-solving abilities with a focus on scalable and efficient system design
- Excellent communication skills with the ability to explain complex technical concepts clearly
- Experience working in cross-functional, global, and collaborative environments
- Strong mentoring mindset and ability to guide technical teams
Responsibilities
- Lead the design, development, and enhancement of AI/ML workflows, models, and algorithms across multiple product lines
- Collaborate on complex geospatial and spatial intelligence problems including vegetation analysis, building footprints, point clouds, and infrastructure modeling
- Partner with cross-functional stakeholders to define priorities, technical approaches, and delivery roadmaps
- Develop and deploy machine learning models in cloud-based distributed environments using modern MLOps practices
- Ensure production readiness of models using containerization technologies such as Docker and Kubernetes
- Explore and implement emerging AI/ML methodologies, tools, and best practices to continuously improve solution quality
- Mentor and coach team members to strengthen machine learning and AI capabilities across the organization
- Contribute across the full software lifecycle, from requirements gathering to deployment and monitoring
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