ROLE / RESPONSIBILITIES
– Join the EO Applied Data Science (EO Data) Team, focusing on foundational research and development in applied machine learning for geospatial data.
– Work on diverse Geospatial Data Science use cases including Land Use and Land Cover (LULC), Crop Classification, Change Detection, Satellite Image Time-Series (SITS) classification, Image2Image (I2I) Translation, and Cross-Modal Fusion.
– Advance next-generation ML applications to surpass State-Of-The-Art, especially in complex geographies.
– Apply research to real-time, large-scale software systems utilizing terabytes of data, with a focus on season, modality, and ground agnostic geospatial data science models.
– Collaborate with applied data scientists, MLOps, geospatial experts, and platform engineers to envision solutions for real-world, ambiguous business use cases with low latency/high throughput.
– Identify and solve assigned problems with simple and elegant solutions, working backwards from desired requirements.
– Propose and validate hypotheses to direct the science roadmap.
– Own time-bound, End-to-End (E2E) solutions for ML applications, including resource and requirements gathering, data collection, cleaning and annotation, model development, and validation.
– Brainstorm, deep dive, implement, and debug fundamentals of systems (e.g., architectures, losses, efficiency, serving, etc.), while writing clean code.
– Define proper output Data Science metrics.
– Clearly communicate findings verbally and in writing to stakeholders of varied backgrounds, with attention to detail.
– Engage and initiate collaborative efforts to meet ambitious applied research and product/client delivery goals.
– Innovate and advance State-Of-The-Art (SOTA) in-house solutions, and communicate findings as IPs (patents, papers), as deemed applicable by business.

REQUIREMENTS / QUALIFICATIONS
– Pursuing M.Tech, MS (Research), PhD in a technical field (e.g., CS, EE, EC, Remote Sensing, etc.), preferably from leading academic/industrial labs/institutes or corporates. Undergraduates/Dual-Degree with relevant research experience may also be considered.
– A proven track record of relevant experience in computer vision, NLP, learning theory, optimization, ML systems, foundational models, etc.
– Technical familiarity with some or most of: Convolutional Neural Networks (CNNs), LSTMs/RNNs/GRUs, Transformers, UNet, YOLO, RCNN, Encoder-Decoder Architectures, Generative Models (GAN, VAE, Diffusion), Contrastive Learning, Self-Supervised Learning, Semi-Supervised Learning, Representation Learning, Image Super Resolution, Traditional Machine Learning (Classification, Regression, Clustering), Active Learning, Learning with Noisy Labels, Multimodal Learning, Synthetic Aperture Radar (SAR)/VV-VH bands, Normalized Difference Vegetation Index (NDVI), False Colour Composite (FCC), Dimensionality Reduction (PCA, UMAP, Isomap), Time-Series Modeling/Forecasting, Model compression (Distillation, Pruning, Quantization), Automatic Mixed Precision training, Fourier Neural Operator (FNO), Climate+AI, Domain Adaptation, Domain Generalization, Anomaly Detection etc. (as evidenced by problem-solving skills in novel scenarios).
– 0-1 years of industry experience, if applicable, will be considered.
– Prior publications in (main tracks/workshops of) ICLR, CVPR, ICCV, ECCV, NeurIPS, ICML, AAAI,