Data scientist

Sat Sure is hiring a Data Scientist to build and deploy end-to-end ML/DL solutions for high-impact use cases across agriculture, infrastructure, and climate. You'll work on real-world data problems using Python, SQL, and deep learning frameworks like Py Torch or Tensor Flow. About Us: Sat Sure is a deep tech, decision Intelligence company that works primarily at the nexus of agriculture, infrastructure, and climate action, creating an impact for the other millions, focusing on the developing world. We want to make insights from earth observation data accessible to all. Join us to be at the forefront of building a deep tech company from India that solves problems for the globe. Roles & Responsibilities: Build and deploy ML/DL models (regression, classification, time-series, CNNs, transformers) to address high-impact business and operational problems. Drive the full data science lifecycle — from problem scoping and data preparation to modeling, validation (A/B tests, back-tests), and production deployment. Develop modular, interpretable pipelines using Python, SQL, and ML libraries (Py Torch, Tensor Flow, scikit-learn), with a focus on scalability and reliability. Work with speed and agility to build quick POCs/MVPs in ambiguous or low-data environments, balancing rigor with iteration. Translate business needs into intelligent systems, embedding models into decision workflows, and communicating results to influence cross-functional decisions. Qualifications: 3 - 5 years of hands-on experience building and deploying ML and DL models (classification, regression, time-series, clustering, CNNs, transformers, etc.) in real-world settings. Must-Have Skills: Built end-to-end ML/DL workflows — from data ingestion and preprocessing to training, evaluation, and deployment. Proficient in Python (pandas, numpy, xarray, scikit-learn, Py Torch/Tensor Flow) and SQL for scalable ML pipelines. Strong foundation in ML theory, deep learning (CNNs, transformers), and statistical modeling (A/B testing, causal inference). Experienced in handling diverse data types with robust preprocessing, feature engineering, and transformation pipelines. Skilled in aligning ML solutions with business goals, building fast POCs, and communicating results for cross-functional impact. Good to Have: Familiar with Postgre SQL, geospatial libraries (geopandas, rasterio), and data visualization tools (Tableau, Power BI, matplotlib, seaborn). Understanding of MLOps, deployment practices, and software development workflows, including version control and agile delivery. Exposure to emerging Agentic AI concepts such as tool-using agents, RAG pipelines, and feedback loops. 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, and bereavement leaves Twice a year appraisal Interview Process: Intro call Assessment Presentation Interview rounds (ideally up to 3-4 rounds) Culture Round / HR round