The successful candidate will be a member of the team responsible for developing end-to-end analytics solutions using Earth observation data, integrating them into the AxelGlobe platform and client-facing products. The role involves transforming complex satellite data into meaningful insights, with a strong emphasis on machine learning supported by data engineering, requiring expertise across both areas to deliver practical, high-impact solutions.
Prior experience in earth observation is not required; candidates with strong backgrounds in computer vision, machine learning, or data science are highly encouraged to apply.
- Design end-to-end solutions in collaboration with the business team to address real-world problems.
- Research, prototype, and validate data processing and analysis algorithms using Earth observation data (e.g., optical imagery, weather, radar).
- Develop and productionize scalable data pipelines and full-stack analysis systems.
- Deliver and maintain deployed solutions, ensuring they meet business requirements and operational needs.
- 3+ years of hands-on experience in image processing within the computer vision domain, including tasks such as classification, regression, segmentation, object detection, and time series analysis.
- Implementing machine‑learning models/algorithms for real‑world problems (beyond basic library‑only use), and hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
- Building and operating image‑data pipelines/MLOps.
- Experience with standard tools and practices of collaborative software development: writing maintainable code, version control (git), code review, testing (e.g., pytest), containers (Docker), CI/CD tools.
- Strong problem-solving and analytical thinking skills.
- Ability to conduct technical discussions in English.
Favorable Skills / Experiences
- 2+ years of professional or research experience in earth observation data processing.
- Experience with the following GIS libraries/tools (or equivalents): GDAL, Rasterio, Geopandas, Shapely.
- Familiarity with AWS services and infrastructure-as-code tools such as Terraform.
- MSc or PhD in a relevant computational field (Computer Science, Remote Sensing, Physics, etc.)
- Deep knowledge of statistics.
- First‑author experience in peer‑reviewed publications.
- Business-level proficiency in Japanese.
- Interest in the space industry, curiosity about other aspects of satellite operations, and openness to cross-team collaboration.
- ML: PyTorch, TensorFlow (CV tasks : segmentation, detection, time series, etc.)
- Geospatial : GDAL, Rasterio, GeoPandas, Shapely
- Data/MLOps : Data pipelines, AWS, Terraform
- Engineering : Python, Git, Docker, CI/CD, pytest
- Earth Observation Analytics & Product Innovation :
- Design and build full-stack analytics solutions using multi-modal Earth observation data (optical satellite imagery, SAR radar, and weather data) to create new value on the AxelGlobe platform.
- Research, prototype, and productionize advanced computer vision and machine learning algorithms to solve complex real-world problems.
- Rapidly translate business needs into scalable, production-ready EO data products.
- End-to-End Data Pipeline Ownership :
- Develop and operate robust, large-scale data processing pipelines and MLOps workflows that handle petabyte-scale satellite imagery and geospatial data.
- Build reliable, maintainable, and observable systems that move seamlessly from experimentation to production.
- Cross-Functional Impact & Space-Tech Collaboration :
- Work closely with business and product teams to identify high-impact use cases and turn cutting-edge EO insights into practical solutions.
- Help shape the future of commercial Earth observation analytics while gaining exposure to satellite operations and the broader space industry.
Data Scientist / ML Engineer
正社員
Full-time, permanent position
報酬：TBD、経験及び能力に応じて面談の上、採用時に決定
Salary: TBD, decided upon the candidate's experience and skill set
Clip Nihonbashi Building, 3-3-3 Nihonbashi-Honcho, Chuo-ku, Tokyo
勤務時間 ：裁量労働制
Work Hour: Discretionary Labor Sytem
休日：週休２日制、祝祭日
休暇：年次有給休暇、慶弔休暇等、5年後ボーナス休暇
Holiday: Two-day off per week, National holidays
Paid Leaves: Annual paid leaves, Congratulatory/Bereavement leaves, 5th-year Bonus leaves
通勤手当支給：実費精算
在宅勤務制度：有
英会話学習補助
Commuting Allowance: Actual-cost reimbursement
Remote Work: Available
Language Support: Japanese class for foreign employees
健康保険
厚生年金
雇用保険
労災保険
Health Insurance
National Welfare Pension
Employment Insurance
Worker's Compensation
