{"id":168901,"url":"https://alion.io/job/satsure-data-scientist-geospatial-foundation-models","title":"Data Scientist – Geospatial Foundation Models","company":{"id":59505,"name":"SatSure","domain":"satsure.co","url":"https://alion.io/company/satsure","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"C","score":64,"open_postings":28,"ghost_share":0.607,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":16},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Pre-training","optional":false}],"status":"live","first_seen_at":"2026-06-19T06:01:57Z","employer_posted_date":"2026-06-19","last_verified_at":"2026-10-17T23:52:48Z","board_verified":true,"closed_at":null,"days_open":120,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":119},"description":"About SatSure\nSatSure is a deep tech, decision intelligence company working at the nexus of agriculture, infrastructure, and climate action - creating impact for the other millions, with a focus on the developing world. As part of this mission, we're building geospatial foundation models that learn directly from Earth observation data - optical, SAR, and elevation - at scale. This role sits at the heart of that effort: architecting and training large-scale models that can generalize across geographies, sensors, and time. You'll be shaping the core intelligence layer that powers insights for millions, not just fine-tuning someone else's model.\nRole\nIn foundation model development, data is the moat. You will drive the transformation of petabytes of raw geospatial data into a high-quality, high-entropy training and evaluation corpus.\nThis role sits at the intersection of remote sensing, data engineering, and ML, ensuring that models learn from diverse, representative, and well-curated data at scale.\nKey Responsibilities\nData Curation & Pre-training Datasets\nDesign and implement data curation pipelines for large-scale pre-training datasets\nDevelop sampling strategies to ensure:Geographic and biome diversity\nCoverage across seasons, sensors, and resolutions\n\nMitigate dataset biases (e.g., over-representation of cloud-free or high-income regions)\nBalance trade-offs between data quality, diversity, and scale\nEvaluation Frameworks (Earth-Bench)\nDesign and own a comprehensive evaluation framework (“Earth-Bench ”) to assess: Representation quality (post-SSL embeddings)\nTransfer performance on downstream tasks:Segmentation\nYield prediction\nDisaster mapping\n\nDefine metrics and benchmarks that reflect real-world generalization across geographies and time\nContinuously evolve evaluation as new datasets, sensors, and tasks emerge\nData Systems & Pipeline Thinking\nBuild and maintain scalable data pipelines for ingestion, processing, versioning, and access\nWork with ML and platform teams to:Enable efficient data loading and training at scale\nOptimize storage formats and access patterns (e.g., chunking, caching)\n\nEnsure datasets are:Reproducible\nWell-documented\nEasily usable across teams\n\nData-Centric ML Thinking\nAnalyze how data quality, diversity, and freshness impact model performance\nPartner with researchers to:Identify failure modes driven by data gaps\nImprove datasets to unlock model gains (not just model changes)\n\nTreat data as a first-class lever for improving model quality\nPreferred Background\nDomain Expertise\n3-5 years of experience in Applied Data Science at scale\nStrong understanding of remote sensing fundamentals, including:Atmospheric correction\nSAR backscatter\nOrthorectification\n\nFamiliarity with multi-sensor data (optical, SAR, DEM, etc.)\nData Engineering at Scale\nExperience working with large-scale (TB-PB) datasets across the ML lifecycle\nHands-on experience with:Distributed data processing\nEfficient storage and retrieval strategies\n\nUnderstanding of how data pipelines interact with model training workflows\nTooling (Geo Stack)\nExperience with geospatial data tooling, such as:Xarray, Dask, Rasterio, Zarr\nGoogle Earth Engine (nice to have)\n\nMindset\nStrong data intuition -ability to reason about bias, coverage, and representativeness\nSystems thinking: understands how data decisions impact model behavior at scale\nComfortable working in ambiguous, evolving problem spaces\nBenefits:\nMedical Health Cover for you and your family, including unlimited online doctor consultations\nAccess to mental health experts for you and your family\nDedicated allowances for learning and skill development\nComprehensive leave policy with casual leaves, paid leaves, marriage leaves, and bereavement leaves\nInterview Process:\nIntro call\nAssessment\nPresentation\nInterview rounds (ideally up to 3-4 rounds)\nCulture Round / HR round","description_format":"text","description_chars":3864,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Geospatial Analytics","AgTech","Earth Observation","Geospatial AI"],"lifecycle":[{"event":"open","at":"2026-08-29T20:50:04Z"}],"visa":[],"liveness":{"score":8,"band":"cold","label":"Long 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