Data Science Manager

Data Science Manager

- As a Data Science Manager, you will be pivotal in developing advanced deep learning and computer vision-based solutions for agriculture, infrastructure, and climate action.

- You will lead a high-performing team of data scientists, ML researchers, and geospatial experts, collaborating with cross-functional teams to address unique agricultural challenges such as crop classification, yield estimation, and sustainability improvements.

- Your work will directly support smallholder farmers by leveraging geospatial data and ML/DL techniques to facilitate access to agricultural financing.

Key Responsibilities:

Deep Learning & Computer Vision Solutions:

- Lead the development of deep learning-based computer vision algorithms to solve real-world applications in agriculture, infrastructure, and climate change.

Scalability & Robustness:

- Apply strong problem-solving skills to develop quick POCs and scalable and robust deep-learning solutions that effectively handle various corner cases.

Mentorship & Leadership:

- Lead and mentor a high-performing team of data scientists and engineers, fostering a collaborative, self-motivated environment.

- People management experience is a plus but not mandatory.

Cross-functional Collaboration:

- Collaborate closely with stakeholders to align data science strategies with business objectives, helping to prioritize efforts and communicate progress on the long-term technology roadmap.

Qualification:

- Advanced degree (MSc/MTech/ME/PhD) in Computer Science, Engineering, Remote Sensing, Geospatial Science, or related fields preferred.

- 5+ years of experience in DL/ML/Data Science, with a strong focus on computer vision.

- 3+ years of tech lead experience in developing and scaling deep learning-based solutions.

Must-Haves:

- Strong foundation in computer vision and deep learning, with hands-on experience building and deploying algorithms for applications like vision foundational models along with supervised and unsupervised image classification, object detection, and segmentation.

- Proficiency in ML frameworks like TensorFlow or PyTorch.

- Expertise in Python and solid statistical knowledge.

- Proven ability to deliver both POCs and production-level solutions.

- Experience with remote sensing, geospatial data, or agriculture domain expertise is a plus but not mandatory.