We are looking for a Senior Data Scientist/TL in our EO Applied Data Science (EO Data) Team. Our mission is realized through foundational research and development in applied machine learning. With a plethora of Geospatial Data Science use-cases that we have solved so far, such as Land Use and Land Cover (LULC), Crop Classification, Sowing and Harvest Progression, Change Detection, Route Optimization, Satellite Image Time-Series (SITS) classification, Image2Image (I2I) Translation, Cross Modal Fusion etc, we are now focusing on advancing the next-generation Machine Learning (ML) applications, and surpass the State-Of-The-Art (SOTA), especially in more ambiguous, complex geographies. We look forward to applying our research to critical products, while touching the lives of millions of users, via revolutionary, real, and near-real time large-scale software systems utilizing Terabytes of data. At the core of such systems, we are envisioning foundational geospatial data science models that are season, modality and ground agnostic. We have been at the forefront of adaptable and efficient models, as evidenced by our findings through publications at top ML/GRS conferences. Roles & Responsibilities: Own a couple of technical roadmaps and charters for assisting the science manager in terms of resource management. Technically mentor science staff and projects. Work in collaboration with product owners, applied data scientists, MLOps, geospatial experts, platform engineers to envision solutions to real-world, ambiguous business use-cases with low latency/ high throughput. Focus on identifying and solving customer problems with simple and elegant solutions, while working backwards from customer requirements. Quickly propose and validate hypotheses to direct the product roadmaps. Own time-bound, End-to-End (E2E) solutioning of large-scale ML applications, ranging from resource, requirements gathering, data collection, cleaning and annotation, model development, validation, deployment, monitoring, etc Brainstorm, deep dive, implement, debug into fundamentals of the systems (eg, architectures, losses, efficiency, serving etc), while writing clean, production level code, and conduct A/B as necessary. Define proper output Data Science metrics and calibrate them to the desired business metrics. Clearly communicate findings verbally and in writing to stakeholders of varied backgrounds. Have 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. Mentor junior staff, interns as applicable. Assist Science managers in effective project, resource management, hiring, and timely deliverables (in an agile manner), via showcasing strong sense of ownership and accountability. Qualification: M.Tech, MS (Research), PhD in a technical field (eg, CS, EE, EC, Remote Sensing, etc), preferably from leading academic/ industrial labs/institutes, corporates. Undergraduates/Dual-Degree with research experience as mentioned below may also be considered. Experience working in industry (2-6 years of experience), on projects from proof-of-concept to deployment and monitoring, while partnering with business stakeholders, product managers, engineers, and other data scientists to translate business needs into technical, deployed solutions. 1-2 years of industry experience as a technical leader, as evidenced by simultaneous, multiple project and/or people management/mentorship instances. Demonstration of functioning in a matrixed organization while aligning cross-functional stakeholders towards shared goals, and exhibiting excellent behavioral skills. Must-have: A proven track record of relevant experience in computer vision, NLP, learning theory, optimization, ML+Systems, foundational models, etc Technically familiar with some, or most of (as evidenced by problem solving skills in novel scenarios): 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 Candidates with prior publications in (main tracks/ workshops of) ICLR, CVPR, ICCV, ECCV, NeurIPS, ICML, AAAI, IJCAI, ACL, EMNLP, TACL, NAACL, TMLR, IGARSS, InGARSS, IEEE Transactions, etc, would have an edge too (with preference to first-authored ones). Proficiency in at least one general programming language (preferably, Python), along with strong hands-on experience with ML frameworks (eg PyTorch) in terms of training large, optimized, scalable, ML models. Strong verbal and written communication skills. Experience with SQL, large scale distributed systems (eg, Spark), MLOps will be handy. Benefits: Medical Health Cover for you and your family including unlimited online doctor consultations Access to mental health experts for you and your family Dedicated allowances for learning and skill development Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves Twice a year appraisal
