{"id":942474,"url":"https://alion.io/job/satsure-lead-data-scientist-2","title":"Lead Data Scientist","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":58,"open_postings":27,"ghost_share":0.704,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","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":{"min":2800000,"max":3200000,"currency":"INR","period":"year","gross":null,"usd_annual":33978},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Computer Vision","optional":false},{"name":"Contrastive Learning","optional":false},{"name":"Encoder-Decoder","optional":false},{"name":"GAN","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Transformers","optional":false},{"name":"YOLO","optional":false}],"status":"live","first_seen_at":"2026-09-15T11:38:08Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-08T17:00:58Z","board_verified":true,"closed_at":null,"days_open":54,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":53},"description":"SatSure is looking for a Lead Data Scientist to drive the next generation of geospatial intelligence models powering critical products across agriculture, forestry, finance, infrastructure, utilities, energy, and climate. This is a senior hands-on technical leadership role responsible for solving complex Earth Observation challenges using advanced ML/CV, and leading teams toward state-of-the-art, production-ready solutions.\nAbout SatSure\nSatSure is a deep-tech decision intelligence company that leverages Earth Observation (EO) data to solve crucial problems across agriculture, forestry, finance, infrastructure, utilities, aviation, energy, and climate, to name a few. Our goal is to create meaningful impact with a focus on the developing world. We aim to make insights from Earth Observation data accessible to all.\nWith a founding team rooted in the Indian Institute of Space Science and Technology (IIST),\nIndian Space Research Organisation (ISRO), and the Indian Institute of Remote Sensing (IIRS), and a leadership team with diverse industry experience (IBM, Samsung, Intel, USC, IITKGP, IITB, IITG, IITM), we value technical innovation and scale. If you are interested in working in an environment focused on societal impact, driven by cutting-edge technology, and offering the freedom to innovate and be creative with no hierarchies, SatSure is the place for you.\nRoles & Responsibilities\nAs a Lead Data Scientist, you will:\nOwn technical charters and roadmap for multiple ML/CV initiatives.\nLead and mentor applied scientists, mapping complex EO problems into actionable, scalable decision systems.\nDrive hypothesis generation, experimentation, architecture design, model development, and deployment for production ML pipelines.\nOwn E2E delivery of large-scale ML/CV systems - from problem framing to data design, model development, deployment, and monitoring.\nCollaborate with Product, MLOps, Platform, and Geospatial experts to convert ambiguous requirements into elegant solutions.\nCommunicate technical findings to leadership, customers, and cross-functional partners with clarity and precision.\nAssist in effective project, resource management, and timely\ndeliverables (in an agile manner), via showcasing strong sense of ownership and accountability.\nBuild reliable, efficient models that scale across geographies, seasons, sensors, and business domains.\nWrite clean, scalable production-grade code in Python/PyTorch.\nConduct A/B experiments and calibrate ML metrics to business KPIs.\nInnovate on model architectures (Transformers, diffusion, generative, time-series ,models, self-supervision, multimodal fusion and temporal modeling) to advance in-house ,geospatial ML SOTA.\nRepresent your work through patents, technical documents, internal whitepapers, and publications (as applicable).\nContribute to hiring and technical excellence, including mentoring junior team members and interns.\nRequired Qualifications\nEducation\nPhD/M.Tech/MS (Research) in CS, EE, EC, Remote Sensing, or related fields preferably from leading academic/industrial labs/institutes/corporates.\nExceptional undergraduates with strong research/industry experience will also be considered.\nExperience\n6+ years of applied ML/Computer Vision experience (industry preferred).\n2+ years in a technical leadership role - people and project leadership.\nProven experience taking ML models from POC → production → monitoring.\nMust-have Technical Expertise\nTo be eligible for this role, we are looking for candidates with the following qualifications:\nA proven track record of relevant experience in computer vision, NLP, learning theory, optimization, ML+Systems, foundational models, etc.\nTechnically familiar with some, or most of (as evidenced by problem solving skills in novel scenarios): Transformers, UNet, RNNs/LSTMs/GRUs,\nYOLO/RCNN/Encoder-Decoder architectures, Generative models (GAN, VAE, Diffusion), Self-supervised & contrastive learning, Representation learning, domain\nadaptation & generalization, Semi-/Active learning, noisy-label learning, Super-resolution, anomaly detection, clustering, Model compression: distillation, pruning,\nquantization.\nPyTorch, Python, SQL, distributed systems (Spark), MLOps for large-scale training, data pipelines, and deployment.\nGood to have:\nSAR (VV/VH), NDVI, FCC, multispectral optical data, Temporal modeling (SITS, forecasting, seasonal dynamics), Cross-modal fusion (SAR+Optical, EO+tabular/ground)\nFirst-authored publications in ICLR, NeurIPS, CVPR, ICCV, ECCV, ICML, AAAI, IGARSS, IEEE TGRS, etc.\nExperience with geospatial datasets, climate models, foundation models, or EO\nanalytics.\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, bereavement leaves\nInterview Process:\nIntro call\nAssessment\nPresentation\nInterview rounds (ideally up to 3-4 rounds)\nCulture Round / HR 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