Data Science Manager

Job Description

We are looking for a Data Science Manager to lead a team in developing and scaling machine learning solutions for utilities and infrastructure. The role involves product development, stakeholder collaboration, team building, and delivering innovative, scalable DS solutions. Roles: 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. Competencies: Fantastic communication skills that enable you to work cross-functionally with business folks, product managers, and technical experts, building solid relationships with a diverse set of stakeholders. The ability to convey complex solutions to a less technical person. Vast analytical problem-solving capabilities experience. Bias for action.