CHIEF GEOSPATIAL DATA SCIENTIST WITH SECURITY CLEARANCE
KBR
Sioux Falls SD 57198 US
Title:
Chief Geospatial Data Scientist Belong. Connect. Grow. with KBR! Around here, we define the future. But we at KBR we share one goal: to improve the world responsibly as a company of innovators, thinkers, creators, explorers, volunteers, and dreamers. Are you a tenured data scientist professional who is looking to flex your AI/ML skills to develop geospatial products that can make a difference in the world? How would you like to leverage over 50 years of continuous Earth monitoring products, afforded by Landsat and other satellite missions, to create high-level geospatial science products to monitor, assess, and forecast land use/land cover change? The ideal candidate will be passionate about leveraging geospatial data to solve complex problems and drive innovation. As the Chief Data Scientist, you will play a key role in shaping our data strategy and driving the development of cutting-edge geospatial analytics solutions for actionable science. If your interests are piqued, keep reading! WORK LOCATION: This position supports a coordinated team across multiple locations and offers either remote work status or a hybrid remote/onsite schedule; some travel may be required. --- Delivering Solutions, Changing the World. We deliver science, technology, and engineering solutions to governments and companies around the world. - https://www.kbr.com/en --- POSITION DESCRIPTION: KBR's Science and Space (S&S) business unit is seeking a highly skilled and experienced Chief Geospatial Data Scientist to lead our data strategy efforts for a diverse range of government agencies, including NASA, NOAA, DOI, DHS, and DOD. As the Chief Geospatial Data Scientist, you will be responsible for leveraging cutting-edge geospatial technologies and data science methodologies to unlock insights from geospatial data and drive impactful decision-making across various government sectors. This thought leader will provide strategic leadership to the development of science-based geospatial data strategies across various KBR government customer domains. This leadership role will be based out of the KBR Technical Support Services Contract (TSSC) with the U.S. Geological Survey (USGS) at the Earth Resources Observation and Science (EROS) Center, located near Sioux Falls, SD (with flexibility to support remote work or a hybrid remote/on-site schedule). This contract provides a wide range of technical services including systems engineering, software development, enterprise architecture development, remote sensing science support, data acquisition, data processing, data archiving, data distribution, and project planning and management. This KBR leadership position is aligned with the Science Division of the TSSC which specializes in the use of a variety of remote sensing data types primarily including land satellite data systems to conduct geospatial-based earth sciences. The Science Division has a wide-ranging portfolio, including: • Land use/land change monitoring, analysis, and forecasting • Landsat science product algorithm research, development, and testing • Fire sciences and other landscape disturbances research • Human health and food security topics • Coastal terrain modeling and monitoring • Water quality and feature dynamics The work includes coordination with a team of exceptionally skilled remote sensing scientists, geospatial data scientists, systems/software engineers, and communications specialists, as well as collaboration with our USGS customers at the EROS Center. Read on to see if you're a match. If you think you have what it takes and are interested in becoming an integral part of our team, we'd love to hear from you! ROLES AND RESPONSIBILITIES: • Data Strategy Development: Develop and implement comprehensive data strategies tailored to the specific needs and objectives of government agencies, with a focus on geospatial data analysis and interpretation. • Geospatial Data Analysis: Lead the analysis of geospatial data sets from diverse sources, including satellite imagery, remote sensing data, GIS data, and other geospatial data repositories, to extract meaningful insights and patterns. • Advanced Data Modeling: Utilize advanced data modeling and machine learning techniques to develop predictive models, spatial analytics, and visualization tools for understanding complex geospatial phenomena and trends. • Cross-Agency Collaboration: Collaborate with internal and external stakeholders, including government agencies, research institutions, and industry partners, to facilitate data sharing, integration, and collaboration initiatives. • Technical Leadership: Provide technical leadership, mentoring, and guidance to teams of geospatial data scientists and analysts, ensuring the successful execution of projects and initiatives. • Innovation and Research: Stay abreast of the latest advancements in geospatial data science, machine learning, and remote sensing technologies, and actively pursue opportunities for innovation and research. • Policy Compliance: Ensure compliance with relevant government regulations, policies, and standards related to geospatial data management, privacy, and security. • Strategic Communication: Communicate complex plans, findings and insights to non-technical stakeholders clearly and concisely. --- PREFERRED SKILLSET: Geospatial Analysis: • In-depth knowledge of spatial data concepts, including coordinate systems, projections, and spatial relationships. • Hands-on experience working with various geospatial data sources such as satellite imagery, GPS data, and digital maps. • Proficiency in utilizing GIS software (e.g., ArcGIS, QGIS, OGC services) for geospatial analysis, visualization, and data management. Machine Learning: • Understanding of fundamental machine learning algorithms (e.g., linear regression, decision trees, SVMs) and their applications in geospatial analysis. • Experience implementing advanced machine learning techniques, including deep learning, for tasks such as image classification and object detection in geospatial data. Programming Languages: • Strong programming skills in Python, including expertise in libraries such as NumPy, Pandas, and SciPy for data manipulation and analysis. • Proficiency in R for statistical analysis and visualization, and SQL for querying spatial databases. Geospatial Libraries: • Familiarity with GDAL (Geospatial Data Abstraction Library) for reading and writing geospatial data formats. • Experience with GeoPandas for manipulating geospatial data in Python, and knowledge of Shapely for geometric operations and Fiona for spatial data files. Machine Learning Libraries: • Understanding of fundamental machine learning algorithms (e.g., linear regression, decision trees, SVMs) and their applications in geospatial analysis • Experience with advanced machine learning techniques such as deep learning for tasks like image classification and object detection in geospatial data • Proficiency in TensorFlow or PyTorch for developing and training deep learning models on geospatial data. • Experience with Scikit-learn for implementing traditional machine learning algorithms in Python. Spatial Data Processing: • Understanding of spatial indexing techniques (e.g., R-tree) for efficient spatial queries. • Knowledge of geocoding techniques for converting addresses into geographic coordinates. Data Visualization: • Ability to create maps and interactive visualizations of geospatial data using libraries such as Matplotlib, Seaborn, and Plotly. • Experience with GIS software for generating professional-quality maps and visualizations. Domain Knowledge: • Understanding of specific earth science domains (e.g., agriculture, urban planning, resource classification) and their geospatial data requirements. • Ability to translate domain knowledge into actionable insights using geospatial AI/ML techniques. Problem Solving: • Strong analytical and problem-solving skills for addressing complex geospatial challenges. • Ability to design and implement innovative solutions using AI/ML approaches for geospatial analysis. Communication Skills: • Effective communication skills for presenting complex geospatial AI/ML concepts and results to diverse audiences, including client-facing situations. • Ability to collaborate with non-technical stakeholders and translate their requirements into technical solutions. Collaboration: • Experience working in interdisciplinary teams, including data engineers, software developers, and geospatial domain experts. • Ability to collaborate effectively with team members and stakeholders to achieve project goals and deliver high-quality solutions. QUALIFICATIONS: • Advanced Degree (Ph.D. preferred) in geospatial science, remote sensing, computer science, data science, or a related field. • 7+ years of experience in data science, with a focus on geospatial analysis. • Proven track record of success in developing and implementing data strategies for government agencies or large organizations, with a focus on geospatial data analytics. • Extensive experience in geospatial data analysis, machine learning, statistical modeling, and data visualization techniques. • Proficiency in geospatial data processing tools and software platforms, such as GIS software (e.g., ArcGIS, QGIS), remote sensing software (e.g., ENVI, ERDAS Imagine), and programming languages (e.g., Python, R). • Strong leadership and communication skills, with the ability to effectively collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders. • Familiarity with government regulations, policies, and standards related to geospatial data management and security (e.g., FGDC, OGC, NIST). Experience and/or Education in lieu of these qualifications will be reviewed for applicability to meet these requirements. KBR Benefits KBR offers a selection of competitive lifestyle benefits which could include 401K plan with company match, medical, dental, vision, life insurance, A
Chief Geospatial Data Scientist Belong. Connect. Grow. with KBR! Around here, we define the future. But we at KBR we share one goal: to improve the world responsibly as a company of innovators, thinkers, creators, explorers, volunteers, and dreamers. Are you a tenured data scientist professional who is looking to flex your AI/ML skills to develop geospatial products that can make a difference in the world? How would you like to leverage over 50 years of continuous Earth monitoring products, afforded by Landsat and other satellite missions, to create high-level geospatial science products to monitor, assess, and forecast land use/land cover change? The ideal candidate will be passionate about leveraging geospatial data to solve complex problems and drive innovation. As the Chief Data Scientist, you will play a key role in shaping our data strategy and driving the development of cutting-edge geospatial analytics solutions for actionable science. If your interests are piqued, keep reading! WORK LOCATION: This position supports a coordinated team across multiple locations and offers either remote work status or a hybrid remote/onsite schedule; some travel may be required. --- Delivering Solutions, Changing the World. We deliver science, technology, and engineering solutions to governments and companies around the world. - https://www.kbr.com/en --- POSITION DESCRIPTION: KBR's Science and Space (S&S) business unit is seeking a highly skilled and experienced Chief Geospatial Data Scientist to lead our data strategy efforts for a diverse range of government agencies, including NASA, NOAA, DOI, DHS, and DOD. As the Chief Geospatial Data Scientist, you will be responsible for leveraging cutting-edge geospatial technologies and data science methodologies to unlock insights from geospatial data and drive impactful decision-making across various government sectors. This thought leader will provide strategic leadership to the development of science-based geospatial data strategies across various KBR government customer domains. This leadership role will be based out of the KBR Technical Support Services Contract (TSSC) with the U.S. Geological Survey (USGS) at the Earth Resources Observation and Science (EROS) Center, located near Sioux Falls, SD (with flexibility to support remote work or a hybrid remote/on-site schedule). This contract provides a wide range of technical services including systems engineering, software development, enterprise architecture development, remote sensing science support, data acquisition, data processing, data archiving, data distribution, and project planning and management. This KBR leadership position is aligned with the Science Division of the TSSC which specializes in the use of a variety of remote sensing data types primarily including land satellite data systems to conduct geospatial-based earth sciences. The Science Division has a wide-ranging portfolio, including: • Land use/land change monitoring, analysis, and forecasting • Landsat science product algorithm research, development, and testing • Fire sciences and other landscape disturbances research • Human health and food security topics • Coastal terrain modeling and monitoring • Water quality and feature dynamics The work includes coordination with a team of exceptionally skilled remote sensing scientists, geospatial data scientists, systems/software engineers, and communications specialists, as well as collaboration with our USGS customers at the EROS Center. Read on to see if you're a match. If you think you have what it takes and are interested in becoming an integral part of our team, we'd love to hear from you! ROLES AND RESPONSIBILITIES: • Data Strategy Development: Develop and implement comprehensive data strategies tailored to the specific needs and objectives of government agencies, with a focus on geospatial data analysis and interpretation. • Geospatial Data Analysis: Lead the analysis of geospatial data sets from diverse sources, including satellite imagery, remote sensing data, GIS data, and other geospatial data repositories, to extract meaningful insights and patterns. • Advanced Data Modeling: Utilize advanced data modeling and machine learning techniques to develop predictive models, spatial analytics, and visualization tools for understanding complex geospatial phenomena and trends. • Cross-Agency Collaboration: Collaborate with internal and external stakeholders, including government agencies, research institutions, and industry partners, to facilitate data sharing, integration, and collaboration initiatives. • Technical Leadership: Provide technical leadership, mentoring, and guidance to teams of geospatial data scientists and analysts, ensuring the successful execution of projects and initiatives. • Innovation and Research: Stay abreast of the latest advancements in geospatial data science, machine learning, and remote sensing technologies, and actively pursue opportunities for innovation and research. • Policy Compliance: Ensure compliance with relevant government regulations, policies, and standards related to geospatial data management, privacy, and security. • Strategic Communication: Communicate complex plans, findings and insights to non-technical stakeholders clearly and concisely. --- PREFERRED SKILLSET: Geospatial Analysis: • In-depth knowledge of spatial data concepts, including coordinate systems, projections, and spatial relationships. • Hands-on experience working with various geospatial data sources such as satellite imagery, GPS data, and digital maps. • Proficiency in utilizing GIS software (e.g., ArcGIS, QGIS, OGC services) for geospatial analysis, visualization, and data management. Machine Learning: • Understanding of fundamental machine learning algorithms (e.g., linear regression, decision trees, SVMs) and their applications in geospatial analysis. • Experience implementing advanced machine learning techniques, including deep learning, for tasks such as image classification and object detection in geospatial data. Programming Languages: • Strong programming skills in Python, including expertise in libraries such as NumPy, Pandas, and SciPy for data manipulation and analysis. • Proficiency in R for statistical analysis and visualization, and SQL for querying spatial databases. Geospatial Libraries: • Familiarity with GDAL (Geospatial Data Abstraction Library) for reading and writing geospatial data formats. • Experience with GeoPandas for manipulating geospatial data in Python, and knowledge of Shapely for geometric operations and Fiona for spatial data files. Machine Learning Libraries: • Understanding of fundamental machine learning algorithms (e.g., linear regression, decision trees, SVMs) and their applications in geospatial analysis • Experience with advanced machine learning techniques such as deep learning for tasks like image classification and object detection in geospatial data • Proficiency in TensorFlow or PyTorch for developing and training deep learning models on geospatial data. • Experience with Scikit-learn for implementing traditional machine learning algorithms in Python. Spatial Data Processing: • Understanding of spatial indexing techniques (e.g., R-tree) for efficient spatial queries. • Knowledge of geocoding techniques for converting addresses into geographic coordinates. Data Visualization: • Ability to create maps and interactive visualizations of geospatial data using libraries such as Matplotlib, Seaborn, and Plotly. • Experience with GIS software for generating professional-quality maps and visualizations. Domain Knowledge: • Understanding of specific earth science domains (e.g., agriculture, urban planning, resource classification) and their geospatial data requirements. • Ability to translate domain knowledge into actionable insights using geospatial AI/ML techniques. Problem Solving: • Strong analytical and problem-solving skills for addressing complex geospatial challenges. • Ability to design and implement innovative solutions using AI/ML approaches for geospatial analysis. Communication Skills: • Effective communication skills for presenting complex geospatial AI/ML concepts and results to diverse audiences, including client-facing situations. • Ability to collaborate with non-technical stakeholders and translate their requirements into technical solutions. Collaboration: • Experience working in interdisciplinary teams, including data engineers, software developers, and geospatial domain experts. • Ability to collaborate effectively with team members and stakeholders to achieve project goals and deliver high-quality solutions. QUALIFICATIONS: • Advanced Degree (Ph.D. preferred) in geospatial science, remote sensing, computer science, data science, or a related field. • 7+ years of experience in data science, with a focus on geospatial analysis. • Proven track record of success in developing and implementing data strategies for government agencies or large organizations, with a focus on geospatial data analytics. • Extensive experience in geospatial data analysis, machine learning, statistical modeling, and data visualization techniques. • Proficiency in geospatial data processing tools and software platforms, such as GIS software (e.g., ArcGIS, QGIS), remote sensing software (e.g., ENVI, ERDAS Imagine), and programming languages (e.g., Python, R). • Strong leadership and communication skills, with the ability to effectively collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders. • Familiarity with government regulations, policies, and standards related to geospatial data management and security (e.g., FGDC, OGC, NIST). Experience and/or Education in lieu of these qualifications will be reviewed for applicability to meet these requirements. KBR Benefits KBR offers a selection of competitive lifestyle benefits which could include 401K plan with company match, medical, dental, vision, life insurance, A