Data Scientist SME

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements (or Qualifications), Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info, not invent or summarize away detail. Remove boilerplate, navigation, legal, EEO, scam warning, duplicated lines, application instructions. We need to parse the description. Key content: - Description paragraph: "Turn complex data into mission advantage..." etc. That's about the role. - "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" - "Clearance Level Required Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination." - "Primary Responsibilities" list: bullet points. - "Basic Qualifications": list. - "Preferred Qualifications": list. - "Pay Range": $131,300.00 - $237,350.00 with note. - "Pay And Benefits": paragraph about benefits. - "About Leidos": paragraph. - There's also "If you're looking for comfort..." etc marketing fluff; maybe we can treat as part of About Leidos? But it's fluff; we should keep substantive info, but not invent. The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." So we need to keep that marketing fluff? It's not boilerplate? It's part of posting but maybe considered filler? It's not boilerplate like EEO, legal, scam warning. It's promotional but still part of posting. However we need to decide if we include it. The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So we keep other text. So we should include the marketing fluff as part of About the role or About Leidos? It appears after Preferred Qualifications and before "#Chinook #NSBA". It's like a tagline. Could be considered part of About Leidos? But there is already an "About Leidos" section later. The marketing fluff: "If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares." This is promotional but not boilerplate; we should keep it. Where to place? Could be part of About the role (intro) or a separate section like "About Leidos". Since there is already an About Leidos paragraph later, we could combine? But we must not invent or summarize away detail; we can keep both. We need to decide sections: About the role (intro description), Responsibilities, Requirements (maybe combine Basic and Preferred Qualifications?), Skills (maybe extract from qualifications), Benefits, Pay, Schedule. Schedule: Not mentioned explicitly. No schedule info. So we should not create Schedule section. Benefits: There is a paragraph about benefits under "Pay And Benefits". Also earlier there is "Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement." That's benefits. So we can include Benefits section. Pay: Pay Range given. Requirements: Could be Basic Qualifications and Preferred Qualifications combined? The instruction: "Prefer a few substantive sections over many thin ones." So we could have a single "Requirements" section that includes both Basic and Preferred Qualifications (maybe split into Basic and Preferred subheadings? But we cannot use headings other than h3 for section headings. Could we have nested? Probably not; we should keep sections as h3 only. Could we have a Requirements section with two paragraphs: Basic Qualifications and Preferred Qualifications. That's acceptable. Skills: Could extract skills from qualifications (Python, etc). But we might avoid duplication. Since we already have qualifications, maybe we don't need separate Skills section. But we need to decide if we include Skills section only if posting has real specific content for it. The posting does list skills (Python, libraries, big data tools, geospatial formats, visualization tools). That's specific. So we could have a Skills section. But we must not repeat info unnecessarily? The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." So we need to keep all info; we can duplicate across sections? That would be repeating but not summarizing away detail. It's okay to repeat? Might be considered duplication but not prohibited. However they said remove duplicated lines (boilerplate). Duplicated substantive info maybe okay but we should avoid unnecessary duplication. Better to structure: About the role (intro), Responsibilities, Requirements (Basic & Preferred), Skills (maybe extracted), Benefits, Pay. But we need to ensure we include location and clearance info somewhere. Where? Could be in About the role or Requirements. The location and clearance are substantive. We should include them. Could add a paragraph after About the role: "Location: Gaithersburg, MD (primary); also Alexandria, VA; Chantilly, VA; Aurora, CO; St. Louis, MO. Clearance: Active Top Secret/SCI with ability to pass Polygraph." That could be part of About the role or a separate section like "Job Details". But we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We could put location and clearance in About the role paragraph. Thus About the role will include the initial description plus location and clearance. Now we need to format using

for section headings,

• for lists,

  for prose. We must not include markdown code fences. Let's draft. First, About the role:

  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. Clearance Level Required: Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination.

  Now Responsibilities: we have list under "Primary Responsibilities". We'll convert each bullet to
• . List items: - 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'll wrap in
  . Now Requirements: We'll combine Basic Qualifications and Preferred Qualifications. Basic Qualifications 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. Preferred Qualifications list: - 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. We'll put them in a single Requirements section with two subheadings? But we cannot use headings other than h3 for section headings. Could we use or

  to label Basic and Preferred? Probably okay. We'll just list them as separate

  with a preceding

  indicating Basic Qualifications and Preferred Qualifications. Now Skills: Could extract skills from qualifications but we already have them listed. To avoid duplication, maybe we skip Skills section and rely on Requirements. But the instruction says to include a section ONLY when posting has real, specific content for it. It does not forbid having overlapping content