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 must not repeat job title as heading. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. We need to parse the description. Key sections: - About the role: The initial paragraph(s) describing role: "Turn complex data into mission advantage..." etc. That's about the role. - Responsibilities: List under "Primary Responsibilities". Those bullet points. - Requirements: Possibly "Basic Qualifications". That's requirements. - Qualifications: Could be "Preferred Qualifications". That's qualifications. - Skills: Might be part of Basic Qualifications (expert proficiency in Python etc). Could be separate but we can include under Skills if we have explicit skill list. The posting includes many skill items under Basic Qualifications. Could treat them as Skills section. - Benefits: There's a "Pay And Benefits" section describing benefits: "Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement." Also earlier "Pay Range" etc. Benefits section should include that. - Pay: Pay Range $131,300.00 - $237,350.00 plus note about guideline. - Schedule: Not mentioned. No schedule info. So we should not include Schedule section. - 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". This is location info; could be part of About the role or maybe a separate section? Not in list of allowed headings; we could embed in About the role as a paragraph. The instruction: Use headings only for those specific sections. So we can include location info in About the role paragraph. - Clearance Level Required: "Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination." This is a requirement; could be under Requirements or Qualifications. Probably under Requirements (basic qualifications). We'll include in Requirements. - Also note about US citizenship required per contract. - Also note about "May possess a Doctorate in technical domain." - Also note about "Original Posting September 16, 2026" etc. That's metadata; we can drop or fold into short p. Probably drop as not substantive. - Also "For U.S. Positions: While subject to change..." etc. That's about posting duration; could be dropped. - Also "Leidos National Security Sector combines..." that's about Leidos; could be part of About the role or maybe separate but not in allowed headings. Could include in About the role. - Also "About Leidos" section: that's about company; could be part of About the role or maybe separate but not allowed headings; we could incorporate into About the role. - Also "Pay And Benefits" section: we already have Benefits and Pay. - Also "Securing Your Data" etc: that's boilerplate scam warning; remove. - Also "Commitment to Non-Discrimination": EEO boilerplate; remove. Thus we need to produce HTML with headings:

About the role

,

Responsibilities

,

Requirements

,

Qualifications

,

Skills

,

Benefits

,

Pay

. Ensure each heading followed by appropriate content:

for prose,

• for lists. We must not invent or summarize away detail. Must preserve all substantive information. So we need to include all details from the posting that are substantive. Let's extract content. About the role: Combine paragraphs: "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. Position may be performed in the following locations: Gaithersburg, MD (primary work location), Alexandria, VA, Chantilly, VA, Aurora, CO, St. Louis, MO." We need to preserve all substantive info; we can combine into a paragraph. Responsibilities: List from "Primary Responsibilities". Each bullet: - 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. We need to preserve all details. Requirements: Basic Qualifications plus clearance and US citizenship. Let's list: - US citizenship is required per contract. - 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. - Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination. We need to keep all. 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 remoting sensing models. - Published research or technical papers in geospatial intelligence, machine learning, or data science. Skills: Could be derived from Basic Qualifications but we already have many skill items there. To avoid duplication, we could treat Skills as a subset: maybe list the technical skills: Python, libraries, big data tools, geospatial tools, visualization tools. But we must not invent or summarize away detail; we can keep the skill details under Skills section, but we need to ensure we don't lose any info. If we put skill details under Skills, we must still have them elsewhere? The instruction: "Preserve ALL substantive information". It doesn't forbid duplication, but better to avoid duplication but not required. However we must not invent or summarize away detail. Duplication is okay as long as we preserve. But we might want to avoid redundancy for clarity. However the instruction says "Prefer a few substantive sections over many thin ones." So we might combine skills into Requirements or Qualifications. But we have separate headings allowed; we could include Skills section with a concise list of skills extracted from Basic Qualifications (like Python, libraries, big data tools, geospatial tools, visualization). However that would be summarizing; we risk losing some detail like "Expert proficiency in Python (or similar languages) and experience with data science libraries (Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, or equivalent technologies)." That's a skill; we can keep it. But we already have those items under Requirements. If we also list them under Skills, we duplicate. That's okay but maybe not ideal. However the instruction says "Prefer a few substantive sections over many thin ones." Having both Requirements and Skills may be thin if we split. Better to put skill details under Requirements (or Qualifications) and not have a separate Skills section unless there is explicit skill content not covered elsewhere. The posting does not have a separate "Skills" heading; the skill details are within Basic Qualifications. So we could omit Skills section entirely, as we have no explicit Skills section beyond what's already in Requirements. The instruction: "include a section ONLY when the posting has real, specific content for it". Since there is no explicit Skills heading, we could decide not to create a Skills section. However the user gave a list of possible headings; they said "Use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." They didn't say we must include all; just that we can use those. So we can omit Skills if no specific content. Thus we will have sections: About the role, Responsibilities, Requirements, Qualifications, Benefits, Pay. Check if we have any content for Benefits: yes. Pay: yes.