Geospatial Data Scientist

Skill: Data Scientist

Location: Mumbai

Experience: 8 to 12 Years

About Us:

LTIMindtree's Geospatial team is a global leader in empowering organizations across industries with next-generation geospatial workflows and information exchange across various industries. Through advanced spatial technologies and GeoAI advancements, we serve diverse sectors including government agencies, construction firms, agricultural enterprises, utilities, telecom providers, smart city initiatives, and mining companies. Our enterprise Geospatial solutions integrate data from multiple sources, convert it into actionable insights, and disseminate information through intuitive geo-analytics dashboards. Committed to providing location intelligent solutions tailored to unique business requirements, we support businesses with end-to-end project management, from data collection to data processing to geo-analytics and geo-visualization through our robust user-friendly map platform, whether it’s tracking your high valued asset in indoor-outdoor environment, monitor a construction site or keeping your projects on schedule. Join us and be part of a team that uses cutting-edge spatial technologies to transform business operations and achieve a competitive edge in the global market.

Job Description:

Are you a skilled Geospatial Data Scientist eager to expand your expertise while making a meaningful impact in the agricultural sector through your geospatial expertise? Do you have a passion for advancing technology within India's primary industry? Our GeoAI team is seeking a skilled data scientist proficient in remote sensing, machine learning, deep learning, computer vision, and spatial analytics to address real-world challenges.

As a Geospatial Data Scientist within our dynamic GeoAI team in a global organization, you'll play a critical role in revolutionizing agricultural practices. You'll collaborate with like-minded professionals on diverse national and international projects, directly impacting the lives of millions worldwide.

Key Responsibilities:

Conduct spatial data analysis and visualization to identify patterns, trends, and relationships.

Develop and implement state-of-the-art machine learning and deep learning algorithms for geospatial data processing and analysis.

Design and optimize geospatial models and algorithms to support decision-making processes.

Process geospatial data from internal and external sources for machine learning systems.

Validate and test models to ensure performance and reliability in production.

Troubleshoot deployed models when results are not matching expectations.

Participate actively in regular review meetings, fostering knowledge sharing and continuous improvement.

Communicate effectively with non-technical coworkers to understand business requirements, present model developments and performance findings to stakeholders through reports, presentations, and visualizations.

Document experimental results, development procedures, and model deployment processes.

Collaborate with other data scientists, developers and cross-functional teams on model development, improvement, and maintenance.

Work with other teams such as Remote Sensing, Agronomy R&D, Web Services, DevOps, and Image Processing.

Stay updated on emerging trends and technologies in geospatial data science and contribute to continuous improvement initiatives.

Requirements:

Bachelor's or master’s degree in Geospatial Science, Geomatics, Computer Science, or related field.

5+ years of relative experience developing several deep learning, and machine learning models in production settings.

Proficiency in Python and deep learning libraries like pandas, Geo pandas, rasterio, scikit-learn, xgboost, pytorch, and tensorflow.

Proven experience in geospatial data analysis, remote sensing, timeseries analysis and GIS technologies.

Familiarity with geospatial data formats, databases, and visualization tools (e.g., ArcGIS, QGIS, PostGIS).

Experience with version control software (e.g. Git)

Experience in writing technical documents.

Understanding of best practices in data science.

Excellent problem-solving skills and ability to work independently or in a team environment.

Preferred Requirements

Experience with cloud computing platforms (e.g., Azure, AWS) and big data processing frameworks (e.g., Hadoop, Spark) is a significant advantage.

Familiarity with advanced remote sensing technologies like SAR and hyperspectral imagery is a plus.

Prior experience within the agricultural sector is highly desirable.