{"id":2913257,"url":"https://alion.io/job/synspective-geospatial-applied-scientist","title":"Geospatial Applied Scientist","company":{"id":3629,"name":"Synspective","domain":"synspective.com","url":"https://alion.io/company/synspective","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"A","score":93,"open_postings":29,"ghost_share":0.034,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":192,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tokyo, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"GDAL","optional":false},{"name":"Git","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Computer Vision","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-09-17T08:23:56Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-10-28T14:14:38Z","board_verified":true,"closed_at":null,"days_open":41,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":40},"description":"The Solution Development Department at Synspective is responsible for developing models and algorithms which produce insights using multiple sources of data, including our own satellite data. To do this, we develop an analytics platform to produce geoscience insights efficiently and easily.\nResponsibility\nUnderstand and organize customer needs from the internal business teams, partners, and customers.\n\nDevelop and improve analytics models using StriX and other data.\n\nSupport communicating analytics with the stakeholders.\n\nDetails of work\nResearch algorithms and develop code to extract insights from SAR imagery.\n\nDetermine a suitable approach for each problem based on literature review and trials. Various tools can be utilized such as image processing, statistical learning, deep learning, etc\n\nEnrich your models by considering other types of information alongside SAR images (optical satellite, infrastructure polygons, weather data, etc)\n\nClosely collaborate with other scientists and also business members to find suitable solutions to various requests or issues.\n\nUtilize version control systems like Git to manage software code and collaborate with team members efficiently.\n\nHelp finding and developing suitable visualization for various models and solutions.\n\nContinuously improve your skills and those of your team.\n\nSelling points of this role\nWork on a daily-basis on imagery produced by Synspective's satellite constellation\n\nCollaborate alongside highly skilled engineers and scientists in a global setting.\n\nParticipate in the development of state-of-the art approaches solving concrete issues related to sustainability, disaster prevention and management, etc.\n\nSome freedom is given to employees on the nature of their tasks and how they solve the various issues assigned to them.\n\nSynspective provides support for learning new skills and passing various certifications\n\nRequirements\nBachelor’s Degree in Data Science, Statistics, Computer Science, or related field.\n\nProduction-ready proficiency in Python, including the scientific Python stack (NumPy/Pandas/Scikit-learn/SciPy).\n\nFamiliarity with common raster and vector data formats, and hand-on experience with GDAL and Python geospatial libraries (rasterio, GeoPandas, shapely).\n\nExperience building analytics with geospatial data.\n\nPreferred qualifications\nMaster’s Degree in Data Science, Statistics, Computer Science, or related field.\n\nExperience with SAR data, pre-processing and utilizing it in earth observation applications.\n\nStrong foundations in both classical ML and deep-learning, with hands-on experience with pytorch and/or other ML frameworks.\n\nExperience applying computer vision/ML methods to geospatial data.\n\nFluent oral and written communication skills in Japanese to convey techniques and results of analyses clearly to both experts and non-experts.","description_format":"text","description_chars":2851,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"Japanese","level":"All levels","optional":true}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Earth Observation"],"lifecycle":[{"event":"open","at":"2026-10-20T10:10:18Z"}],"visa":[],"liveness":{"score":84,"band":"hot","label":"Hiring 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