Data Scientist SME

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to extract content from posting. We must not repeat job title as heading. No Job ID etc. Remove boilerplate. We need to preserve all substantive info. Let's parse. Description: "Turn complex data into mission advantage..." That's about the role. Could be About the role. Then "Primary Responsibilities" list bullet points. Then "Basic Qualifications" list. Then "Preferred Qualifications" list. Then "Pay Range" with numbers. Then "Pay And Benefits" paragraph about benefits. Also "Location" info: "Position may be performed in the following locations below. Please note Gaithersburg, MD is the primary work location for the program. Gaithersburg, MD Alexandria, VA Chantilly, VA Aurora, CO St. Louis, MO". That's location info; could be Schedule? Not schedule. Could be maybe "Schedule" not present. No explicit schedule. So we may omit Schedule. Benefits: we have paragraph about benefits: "Pay and benefits are fundamental..." and list: competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. Also link. Thus Benefits section. Pay: we have Pay Range $131,300.00 - $237,350.00 plus note. Thus Pay section. About the role: we have description paragraph(s). Could combine first paragraph and maybe the Leidos National Security Sector paragraph? That is about company but could be part of About the role? Might be considered "About Leidos". But we only have sections defined; we could put company info under About the role or maybe not include if not specific to role. The instruction: "Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we can include About the role with role description. The Leidos National Security Sector paragraph is more about the sector; could be part of About the role or maybe we could omit as not directly role? But it's substantive info about the team. Might be okay to include in About the role. Requirements vs Qualifications: The posting has Basic Qualifications and Preferred Qualifications. We could map Basic Qualifications to Requirements (or Qualifications). Preferred Qualifications to Qualifications? Actually they said Requirements, Qualifications, Skills. We need to decide. Could treat Basic Qualifications as Requirements, Preferred Qualifications as Qualifications, and Skills maybe from expertise list? But we have many skill items in Basic Qualifications (Python, etc). Could create Skills section extracting those. However we must not invent sections without content. We have skill details; we could put them under Skills. But we also have Requirements (maybe clearance, citizenship, location). The posting includes "Clearance Level Required Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination." Also US citizenship required. Also locations. Thus Requirements section could include citizenship, clearance, location. Qualifications could include education/experience (Basic Qualifications). Preferred Qualifications could be separate but we could merge into Qualifications? But we have both Basic and Preferred; we could put Basic under Qualifications and Preferred under Skills? Not ideal. Better: Use Requirements for mandatory items (citizenship, clearance, location). Use Qualifications for education/experience (Basic Qualifications). Use Skills for technical skills list (Python, libraries, big data tools, geospatial formats, visualization, leadership). Use Preferred Qualifications maybe as additional notes under Qualifications or Skills? Could add a subsection? But we must not create extra headings beyond allowed list. Could incorporate Preferred Qualifications into Qualifications as a note. Let's extract content. About the role: combine first paragraph(s) up to "This is an opportunity to work at the intersection..." plus maybe the Leidos National Security Sector paragraph. Let's copy: "Turn complex data into mission advantage. Join Leidos on the Chinook Program supporting our Intelligence Community customer and play a critical role in transforming massive, complex datasets into actionable intelligence and mission insights. As a Data Scientist SME, you will bring advanced analytical expertise, innovative thinking, and technical leadership to a high performing Agile engineering team delivering next generation capabilities to our Intelligence Community customer. This is an opportunity to work at the intersection of data science, artificial intelligence, machine learning, cloud technology, and national security, where your expertise can directly influence how mission users understand data, identify patterns, and make critical decisions. You will serve as a technical authority while collaborating with software engineers, DevSecOps professionals, system architects, analysts, and mission stakeholders to solve challenging problems that don’t have off-the-shelf answers. If you are energized by applying advanced analytics to real-world mission challenges and want to see your work have a tangible impact, this is an opportunity to make a difference. Leidos National Security Sector combines technology-enabled services and mission software capabilities in the areas of cyber, logistics, security operations, and decision analytics to support our defense and intel customers’ mission to defend against evolving threats around the world. Our team’s focus is to ensure our customers have the right tools, technologies, and tactics to keep pace with an ever-evolving security landscape and succeed in their pursuit to protect people and critical assets." That's good. Responsibilities: list bullet points from "Primary Responsibilities". We'll extract each sentence starting with a verb. List: - Serve as the technical SME in data science and geospatial analytics, providing high-level guidance on complex technical problems related to the GEOINT mission. - Lead the design and development of sophisticated data science models and machine learning algorithms to analyze large, multi-source geospatial data sets (e.g., satellite imagery, sensor data, geospatial databases). - Apply deep knowledge of GEOINT, remote sensing, and spatial analysis to develop and implement solutions that enhance data-driven decision-making capabilities. - Provide strategic recommendations for leveraging new and emerging technologies, including machine learning, AI, and cloud platforms, to improve analytical workflows, efficiency, and mission outcomes. - Mentor and train junior data scientists and analysts, ensuring the application of best practices in data science and the development of mission-relevant expertise. - Work closely with GEOINT analysts, program managers, engineers, and other stakeholders to identify critical requirements, define technical approaches, and ensure that solutions align with mission objectives. - Conduct applied research to explore innovative data science techniques and emerging trends (e.g., deep learning, reinforcement learning, advanced geospatial algorithms) that can be applied to solve real-world intelligence problems. - Oversee the deployment and operationalization of data science models, ensuring they are scalable, reliable, and deliver actionable insights to GEOINT decision-makers. - Maintain high-quality documentation for models, methodologies, and analysis processes to support reproducibility, training, and knowledge transfer. Requirements: citizenship, clearance, location. - US citizenship is required per contract. - Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination. - Position may be performed in the following locations: Gaithersburg, MD (primary work location), Alexandria, VA, Chantilly, VA, Aurora, CO, St. Louis, MO. Qualifications: Basic Qualifications (education/experience). We'll list as bullet points. - Bachelor’s degree in Data Science, Computer Science, Geospatial Science or related field and 12-15 years of prior relevant experience or Master’s with 10-13 years of prior relevant experience. May possess a Doctorate in technical domain. - 10+ years of professional experience in data science. - 5+ years of experience with GEOINT or geospatial data analysis. - Proven expertise in developing and implementing machine learning and AI models, particularly in context of geospatial or remote sensing. - Deep knowledge of geospatial analytics tools (e.g., GIS, ArcGIS), remote sensing techniques, and the application of data science in the IC. - Expert proficiency in Python (or similar languages) and experience with data science libraries (Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, or equivalent technologies). - Strong experience with big data processing tools (e.g., Spark, PySpark, Kafka, Hadoop, AWS or Azure cloud platforms). - Expertise in working with geospatial data formats (e.g., GeoTIFF, Shapefiles, WMS, WFS) and spatial libraries (e.g., GeoPandas, Rasterio, GDAL). - Advance experience in developing and operationalizing AI/ML models and algorithms for geospatial data (e.g., object detection from satellite imagery, spatial clustering, predictive analytics). - Strong background in data visualization and reporting tools (e.g., Tableau, PowerBI). - Strong leadership, communication, and collaboration skills, with the ability to work directly with senior government officials, analysts, and technical teams. - Excellent problem-solving and analytical skills with a demonstrated ability to translate technical challenges into actionable insights. Skills: maybe we can extract the technical skills list from above but we already included many in Qualifications. To avoid duplication, we could create Skills section focusing on specific technical proficiencies: Python, libraries, big data tools, geospatial formats, visualization tools, leadership etc. But that duplicates. Perhaps better to treat Qualifications as education/experience, and Skills as technical proficiencies list (Python, libraries, big data, geospatial, visualization). Then Requirements as citizenship/clearance/location. Responsibilities as is. About the role as is. Benefits and Pay separate. Thus we need to split Basic Qualifications into two: Qualifications (education/experience) and Skills (technical). Let's do that. Qualifications (education/experience): - Bachelor’s degree in Data Science, Computer Science, Geospatial Science or related field and 12-15 years of prior relevant experience or Master’s with 10-13 years of prior relevant experience. May possess a Doctorate in technical domain. - 10+ years of professional experience in data science. - 5+ years of experience with GEOINT or geospatial data analysis. - Proven expertise in developing and implementing machine learning and AI models, particularly in context of geospatial or remote sensing. - Strong leadership, communication, and collaboration skills, with the ability to work directly with senior government officials, analysts, and technical teams. - Excellent problem-solving and analytical skills with a demonstrated ability to translate technical challenges into actionable insights. Skills (technical): - Deep knowledge of geospatial analytics tools (e.g., GIS, ArcGIS), remote sensing techniques, and the application of data science in the IC. - Expert proficiency in Python (or similar languages) and experience with data science libraries (Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, or equivalent technologies). - Strong experience with big data processing tools (e.g., Spark, PySpark, Kafka, Hadoop, AWS or Azure cloud platforms). - Expertise in working with geospatial data formats (e.g., GeoTIFF, Shapefiles, WMS, WFS) and spatial libraries (e.g., GeoPandas, Rasterio, GDAL). - Advance experience in developing and operationalizing AI/ML models and algorithms for geospatial data (e.g., object detection from satellite imagery, spatial clustering, predictive analytics). - Strong background in data visualization and reporting tools (e.g., Tableau, PowerBI). Preferred Qualifications could be added as a note under Qualifications or Skills, but we could add a separate section? Not allowed headings beyond list. Could incorporate into Qualifications as a bullet "Preferred qualifications include..." but we need to preserve content. Let's add a bullet under Qualifications: "Preferred qualifications: Advanced certifications in data science or machine learning; Familiarity with the customer’s mission and specific geospatial intelligence challenges; Expertise in advanced deep learning techniques, particularly in computer vision and image analysis for geospatial applications; Experience with cloud-native applications and distributed computing in a geospatial context; Familiarity with satellite data analysis, and remote sensing models; Published research or technical papers in geospatial intelligence, machine learning, or data science." Now Benefits: we have paragraph: "Pay and benefits are fundamental to any career decision.