Data Science Intern

About SatSure:

SatSure is a deep tech decision intelligence company that leverages EarthObservation (EO) data to solve crucial problems at the nexus of agriculture finance infrastructure utilities aviation energy and climate to name a few. Our goal is to create an impact for the other millions focusing on the developing world. We want to make insights from earth observation data accessible to all.

With a founding team having roots in the Indian Institute of Space Science and Technology (IIST) Indian Space Research Organisation (ISRO) Indian Institute of Remote Sensing (IIRS) and the leadership team having diverse backgrounds (IBM Samsung Intel USC IITKGP IITG IITM) we value technical innovation and scale. If you are interested in working in an environment that focuses on the impact on society driven by cuttingedge technology and where you will have the freedom to work on innovative ideas and be creative with no hierarchies SatSure is the place for you.

Data Science Intern EO Data Team:

We are looking for an Data Science Intern 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 TimeSeries (SITS) classification Image2Image (I2I) Translation CrossModal Fusion etc we are now focusing on advancing the nextgeneration Machine Learning (ML) applications and surpass the StateOfTheArt (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 nearrealtime largescale software systems utilizing Terabytes of data. At the core of such systems we envision 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.

Key Responsibilities:

• Work in collaboration with applied data scientists MLOps geospatial experts and platform engineers to envision solutions to realworld ambiguous business use cases with low latency/ high throughput.
• Focus on identifying and solving assigned problems with simple and elegant solutions while working backwards from desired requirements.
• Quickly propose and validate hypotheses to direct the science roadmap. Own timebound EndtoEnd (E2E) solutions for ML applications ranging from resource requirements gathering data collection cleaning and annotation model development and validation.
• Brainstorm deep dive implement and debug into fundamentals of the 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. Have attention to detail.
• Engage and initiate collaborative efforts to meet ambitious (applied research and product/client delivery) goals.
• Innovate and advance StateOfTheArt (SOTA) inhouse solutions and communicate findings as IPs (patents papers) as deemed applicable by business.

About You:

To be eligible for this role we are looking for candidates with the following qualifications:

• PursuingM.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/DualDegree with research experience as mentioned below may also be considered.
• 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 problemsolving skills in novel scenarios): Convolutional Neural Networks (CNNs) LSTMs/RNNs/GRUs Transformers UNet YOLO RCNN EncoderDecoder Architectures Generative Models (GAN VAE Diffusion) Contrastive Learning SelfSupervised Learning SemiSupervised Learning Representation Learning Image Super Resolution Traditional Machine Learning (Classification Regression Clustering) Active Learning Learning with Noisy Labels Multimodal Learning Synthetic Aperture Radar (SAR)/VVVH bands Normalized Difference Vegetation Index (NDVI) False Colour Composite (FCC) Dimensionality Reduction (PCA UMAP Isomap) TimeSeries Modeling/ Forecasting Model compression (Distillation Pruning Quantization) Automatic Mixed Precision training Fourier Neural Operator (FNO) ClimateAI Domain Adaptation Domain Generalization Anomaly Detection etc.
• Experience working in industry 01 years of experience) if applicable will also be considered.
• 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 firstauthored ones).
• Proficiency in at least one general programming language (preferably Python) along with strong handson experience with ML frameworks (e.g. PyTorch) in terms of training large optimized scalable ML models.
• Experience with SQL largescale distributed systems (e.g. Spark) and MLOps will be handy.
• Strong verbal and written communication skills.