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Remote Sensing Analyst
Sacramento, CA, USA
Land IQ, LLC is seeking a Remote Sensing Analyst in its Sacramento, California office with a specialization in data driven analytics and algorithm development with digital images for agricultural and other land use applications (crop classification, yield modeling, land use impacts, land use change, etc.).
Land IQ specializes in providing solutions to challenging agricultural and environmental problems throughout the western United States. Our areas of expertise include soil science, water quality and demand evaluation, agricultural systems, salinity and nutrient management, ecosystem restoration, statistics, remote sensing, geospatial analysis, and regulatory policy.
The successful Remote Sensing Analyst candidate will be responsible for working with our growing geospatial team comprised of remote sensing/GIS experts and a range of agronomic, environmental, and other land-based science disciplines. Applicants must have strong analytical, remote sensing and GIS skills and an exceptionally strong understanding of machine learning algorithms. This is an environmental consulting position requiring ability to develop analysis approaches/methodology and work within a team to optimize methods. The applicant must have good communication skills, readily work in a team environment, demonstrate ability to manage multiple tasks and perform work on time and within budget resources.
Office Locations: Sacramento, CA
Hiring Timeframe: Immediate
Employment Type: Full time, including benefits
- Work in a team environment on a wide range of projects, supporting our team of scientists, remote sensing analysts, and GIS analysts
- Organized and methodical with communications and work documentation
- Understand geospatial challenges and conceptualize and articulate analysis approach ideas within a multi-disciplinary team
- Develop and perform raster-based imagery analysis procedures, spatial and statistical modeling applications
- Perform and develop advanced object-based image analysis procedures and methodologies for earth science applications
- Develop and implement remote sensing and statistical methodologies to perform land use and land cover classification (crop classification)
- Develop innovative image analysis solutions using a wide array of data sources
- Resourceful in seeking, preparing, and/or creating raster & vector data
- Leverage a strong understanding of multispectral imagery characteristics to solve complex agricultural and environmental land-based problems
Education: BS/MS Remote Sensing/Geography/Data Sciences field (advanced degree preferred), Research Experience Preferred.
Experience: 2+ years of experience in at least one of the following areas:
- Remote sensing based land cover mapping over agricultural area and crop classification;
- Image based time series analysis and crop phenology modeling;
- Remote sensing of crop evapotranspiration; and/or
- Image based crop yield modeling.
Required Technical Capabilities:
- Advanced spatial analysis and geoprocessing skills, experiences with ERDAS Imagine and ArcGIS software.
- Proficient at machine learning algorithms, familiar with algorithms like Generalized Linear Model, Random Forest, CART, and/or deep learning.
- Strong programming skills with Python or R.
- Experienced in Python packages like NumPy, SciPy, Pandas, Matplotlib, Scikit-Learn, Keras; or similar packages in R programming environment like dplyr, ggplot, randomForest, XGBoost, etc.
Positive/enthusiastic attitude, passionate about professional pursuits, strong organizational, communication and writing skills, personable, ability and desire to learn, and attention to detail, strong moral and ethical personal standards. Enthusiastic about guiding and mentoring team members to reach team and organizational goals. Team player.
Please send resume/cover letter to firstname.lastname@example.org. Also visit our website for more information at www.landiq.com.
Apply via email to email@example.com
2020 L Street, Ste 110
Sacramento, CA, 95811 USA
Posted 2018-08-02 under Remote Sensing