Data Scientist SME

We need to output HTML fragment with sections: headings using

for section headings: 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. No Job ID etc. Remove boilerplate. Preserve all substantive info. Must not invent or summarize away detail. Must not include filler like "commensurate with experience". Must not create Pay/Schedule/Benefits unless posting actually states them. The posting includes Pay Range and Benefits description. So we can include Pay and Benefits sections. Schedule not mentioned, so omit Schedule. Also we have location info, clearance level. Those could go under About the role or maybe Requirements? The instructions: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content accordingly. We have description: "Turn complex data into mission advantage..." That's about the role. Could be About the role. Responsibilities: list of primary responsibilities bullet points. Requirements: maybe basic qualifications? But they also have Basic Qualifications and Preferred Qualifications. Could treat Basic Qualifications as Requirements, and Preferred Qualifications as Qualifications? Or Skills? The headings list includes Requirements, Qualifications, Skills. We need to decide. We have: Basic Qualifications (US citizenship, degree, years experience, etc). Preferred Qualifications (certifications, familiarity, etc). Also there is a list of skills: expertise in Python, big data tools, geospatial formats, etc. Those could go under Skills. We need to preserve all substantive info. So we must include all details from Basic Qualifications, Preferred Qualifications, and the skill bullets. We must not repeat job title. No mention of Job ID. We must remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning etc. So we need to cut out the sections about Leidos, About Leidos, Pay And Benefits (but we need to keep Pay and Benefits? Actually Pay And Benefits section includes description of benefits; we need to keep Pay and Benefits as per instruction: include Pay and Benefits sections only if posting actually states them. The posting does state Pay Range and a paragraph about Pay and benefits. So we should include Pay and Benefits sections. However the instruction says use headings Pay and Benefits separately? They listed Pay, Benefits as separate possible headings. We could combine? It says "Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we can have separate

Pay

and

Benefits

. The posting includes Pay Range and a paragraph about benefits. So we need to output both. We must not include filler like "commensurate with experience". Not present. We must not include location as heading; we can include location in About the role paragraph or maybe as a

within About the role. Clearance Level Required: could be part of Requirements. We need to ensure we don't create sections with no content. Let's extract content. About the role: The initial description paragraphs up to "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." Also maybe the Leidos National Security Sector paragraph? That is about the team focus. Could be part of About the role. We'll include that. Responsibilities: The list under "Primary Responsibilities". We'll turn each sentence into

. Requirements: Could be Basic Qualifications? But Basic Qualifications includes education, experience, etc. Also includes clearance level. We'll put those under Requirements. Qualifications: Preferred Qualifications. Skills: The list of technical skills: expertise in Python, big data processing tools, geospatial data formats, data visualization tools, leadership, communication, problem solving. Those are part of Basic Qualifications but we can extract them into Skills section. However we must not duplicate content. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. If we move some bullets to Skills, we still preserve them. It's okay as long as we don't lose any info. We'll need to ensure we still have the info somewhere. Could keep them under Skills and not duplicate elsewhere. But Basic Qualifications also includes those skill bullets; we could treat Basic Qualifications as Requirements and then also have Skills section that repeats them? That would duplicate. Better to split: Requirements: education, experience, citizenship, clearance. Skills: technical skills list. Qualifications: Preferred Qualifications. Thus we need to parse Basic Qualifications into two groups: education/experience/citizenship/clearance (requirements) and technical skills (skills). The Basic Qualifications paragraph includes many bullet-like sentences separated by periods. We'll extract. Let's list Basic Qualifications sentences: - 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 tecchnologies). - 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. We can separate: Requirements: US citizenship, degree/experience ranges, 10+ years data science, 5+ years GEOINT, maybe also Doctorate optional? Could be included as qualification but it's optional. We'll keep as requirement: "May possess a Doctorate in technical domain." Could be a qualification but it's optional; we can keep under Requirements as "May possess a Doctorate in technical domain." Skills: the rest: expertise in ML/AI models, deep knowledge of geospatial analytics tools, expert proficiency in Python and libraries, strong experience with big data processing tools, expertise in geospatial data formats and spatial libraries, advance experience in developing/operationalizing AI/ML models for geospatial data, strong background in data visualization/reporting tools, strong leadership/communication/collaboration skills, excellent problem-solving/analytical skills. Now 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. Now Benefits: The posting includes a paragraph under "Pay And Benefits": "Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at www.leidos.com/careers/pay-benefits." We need to keep that as Benefits section. Pay section: Pay Range $131,300.00 - $237,350.00 plus note about guideline. We'll output:

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), 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.

But we moved clearance to Requirements maybe. However we included clearance in About the role; but we also need to keep it somewhere. If we put it in About the role, we must not duplicate elsewhere. We could keep it in About the role and not have a separate Requirements for clearance. However Requirements should include education/experience etc. We'll keep clearance in About the role to avoid duplication. But the instruction says we should not invent or summarize away detail; we can place it anywhere as long as it's preserved. So we can keep it in About the role. Now Responsibilities: list each bullet. Let's extract responsibilities sentences: - 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