Lead Data Scientist
SatSure (company site, opens in a new tab)6+ yrsBengaluruOn-siteDirect career pageEst. 25–45 LPA
Posted on 15 Sept
Reach people at SatSure
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at SatSure you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead multiple machine learning and computer vision initiatives for geospatial intelligence applications across domains like agriculture, forestry, and climate.
- Own end-to-end delivery of large-scale ML systems including problem framing, data design, model development, deployment, and monitoring.
- Lead and mentor technical teams, collaborate cross-functionally, drive experimentation, and communicate findings to leadership and external partners.
Minimum Requirements
- PhD/M.Tech/MS (Research) in CS, EE, EC, Remote Sensing, or related fields; exceptional undergraduates with strong experience considered.
- 6+ years of applied ML/Computer Vision experience in industry preferred.
- 2+ years in technical leadership role involving people and project management.
- Proven expertise with Transformers, PyTorch, Python, distributed systems, and deployment of production ML models.
Ideal Candidate Profile
- Experienced in advancing geospatial ML/CV state-of-the-art, handling ambiguity and scaling solutions across geographies and sensors.
- Strong track record in research or industry with ability to innovate model architectures including generative and temporal models.
- Capable of leading technical teams while managing complex projects and contributing to organizational knowledge via patents or publications.