Data Scientist 3-FFPP-8955

We need to output HTML fragment with sections: About the role? The posting includes description of responsibilities, requirements, qualifications, benefits, pay. No schedule. Must not repeat job title. Must not include boilerplate. Must include only sections with real content. We need to parse the posting. First line: "Required TO BE CONSIDERED FOR THIS POSITION YOU MUST HAVE AN ACTIVE TS/SCI W/ FULL SCOPE POLYGRAPH SECURITY CLEARANCE (U.S. CITIZENSHIP REQUIRED)" This is a requirement. Could be part of Requirements or Qualifications. Then: "A data scientist will develop machine learning, data mining, statistical and graph-based algorithms to analyze and make sense of datasets; prototype or consider several algorithms and decide upon final model based on suitable performance metrics; build models or develop experiments to generate data when training or example datasets are unavailable; generate reports and visualizations that summarize datasets and provide data-driven insights to customers; partner with subject matter experts to translate manual data analysis into automated analytics; implement prototype algorithms within production frameworks for integration into analyst workflows." That's responsibilities. Then: "Bachelor's degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). A minimum of ten (10) years of experience in two (2) or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, advanced statistical analysis, artificial intelligence, or software engineering with data analysis software such as R, Python, SAS, or MATLAB. An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree. A Master’s Degree from an accredited college or university in a quantitative discipline can be substituted for two (2) years of experience for a total of eight (8) years of experience required. A Doctoral Degree from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience for a total of six (6) years of experience required." That's qualifications. Then: "Produce data visualizations that provide insight into dataset structure and meaning Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs) Incorporate SME input into feature vectors suitable for analytic development and testing Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics Develop statistical tests to make data-driven recommendations and decisions Develop experiments to collect data or models to simulate data when required data are unavailable Develop feature vectors for input into machine learning algorithms Identify the most appropriate algorithm for a given dataset and tune input and model parameters Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices) Oversee the development of individual analytic efforts and guide team in analytic development process Guide analytic development toward solutions that can scale to large datasets Partner with software engineers and cloud developers to develop production analytics Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation Lead a team of data scientists in the development of multiple analytic efforts Work with customers and SMEs to define analytic requirements and guide the team in formulating analytics that meet requirements Guide the transition of prototyped analytics to production system Understand emerging machine learning and pattern recognition algorithms and guide a team of data scientists in integrating state-of-the-art algorithms into solutions Delegate analysis responsibilities to one or more team members and monitor performance" These are more responsibilities/duties. Then: "Compensation: We are committed to providing fair and competitive compensation. The salary range for this position is $78,000 to $250,000 per year. This range reflects the compensation offered across the locations where we hire. The exact salary will be determined based on the candidate's work location, specific role, skill set, and level of expertise." That's Pay. Then: "Benefits: We offer a comprehensive benefits package, including: Health Coverage: Medical, dental, and vision insurance Additional Insurance: Basic Life/AD&D, Voluntary Life/AD&D, Short and Long-Term Disability, Accident, Critical Illness, Hospitalization Indemnity, and Pet Insurance Retirement Plan: 401(k) plan with company match Paid Time Off: Generous PTO, paid holidays, parental leave, and more Wellness: Access to wellness programs and mental health support Professional Development: Opportunities for growth, including tuition reimbursement Additional Perks Flexible work arrangements, including remote work options Flexible Spending Accounts (FSAs) Employee referral programs Bonus opportunities Technology allowance A diverse, inclusive, and supportive workplace culture" That's Benefits. No schedule. We need sections: About the role? Could be a short paragraph summarizing role. But we have responsibilities. Could have sections: Responsibilities, Requirements, Qualifications, Benefits, Pay. Possibly also "Security Clearance" as part of Requirements. But we can embed that in Requirements. We must not create sections with filler. So we need to decide which headings to include. We have content for Responsibilities (multiple paragraphs). We have Requirements (security clearance, citizenship). We have Qualifications (education/experience). We have Benefits. We have Pay. We could combine Requirements and Qualifications into one "Qualifications" section, but the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can choose which to include. We have both Requirements (clearance) and Qualifications (education/experience). Could separate them. We also have Skills? Not explicitly listed as separate; but responsibilities include many skills. Could create a Skills section summarizing technical skills: machine learning, data mining, statistical, graph-based algorithms, R, Python, SAS, MATLAB, software development, cloud development, etc. But we must not invent or summarize away detail; we can extract from text. The posting includes many skill mentions; we could create a Skills section with bullet list of those skills. However we must preserve all substantive information; we can move some details to Skills but must not lose them. Might be okay to have a Skills section summarizing the technical skills mentioned. But we need to avoid duplication. If we put skills in Skills section, we should not repeat them elsewhere. However the responsibilities also mention many of those skills; we could keep responsibilities as prose describing duties, and Skills as bullet list of technical competencies. That seems acceptable. We must not create sections with filler like "commensurate with experience". We have actual content for Pay and Benefits. Thus final HTML could be:

Responsibilities

• ... each duty as li?

Or we could use

for prose and

• for list. The instruction: Use
  • for any list of duties/requirements/benefits, and

    for prose. So we need to decide if we present duties as list or prose. The posting has many duties separated by semicolons; we could turn each into

  • . That would be a list. Similarly for requirements/qualifications we could list. But we must preserve ALL substantive information; we can break sentences into list items. Let's extract duties: From first paragraph: - Develop machine learning, data mining, statistical and graph-based algorithms to analyze and make sense of datasets - Prototype or consider several algorithms and decide upon final model based on suitable performance metrics - Build models or develop experiments to generate data when training or example datasets are unavailable - Generate reports and visualizations that summarize datasets and provide data-driven insights to customers - Partner with subject matter experts to translate manual data analysis into automated analytics - Implement prototype algorithms within production frameworks for integration into analyst workflows From second block (starting "Produce data visualizations..."): - Produce data visualizations that provide insight into dataset structure and meaning - Work with subject matter experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs) - Incorporate SME input into feature vectors suitable for analytic development and testing - Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes - Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics - Develop statistical tests to make data-driven recommendations and decisions - Develop experiments to collect data or models to simulate data when required data are unavailable - Develop feature vectors for input into machine learning algorithms - Identify the most appropriate algorithm for a given dataset and tune input and model parameters - Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices) - Oversee the development of individual analytic efforts and guide team in analytic development process - Guide analytic development toward solutions that can scale to large datasets - Partner with software engineers and cloud developers to develop production analytics - Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation - Lead a team of data scientists in the development of multiple analytic efforts - Work with customers and SMEs to define analytic requirements and guide the team in formulating analytics that meet requirements - Guide the transition of prototyped analytics to production system - Understand emerging machine learning and pattern recognition algorithms and guide a team of data scientists in integrating state-of-the-art algorithms into solutions - Delegate analysis responsibilities to one or more team members and monitor performance That's a lot; we can keep as list. Requirements: - Must have an active TS/SCI with full scope polygraph security clearance - U.S. citizenship required Qualifications: - Bachelor's degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science) - Minimum of ten (10) years of experience in two (2) or more of the following: designing/implementing machine learning, data mining, advanced analytical algorithms, advanced statistical analysis, artificial intelligence, or software engineering with data analysis software such as R, Python, SAS, or MATLAB - An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree - A Master’s Degree from an accredited college or university in a quantitative discipline can be substituted for two (2) years of experience for a total of eight (8) years of experience required - A Doctoral Degree from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience for a total of six (6) years of experience required Skills (maybe): - Machine learning - Data mining - Statistical analysis - Graph-based algorithms - Advanced analytical algorithms - Artificial intelligence - Software engineering with R, Python, SAS, MATLAB - Software development - Cloud development - Analyzing datasets - Developing descriptive, predictive, and prescriptive analytics - Data visualization - Feature vector development - Model selection and tuning - Performance evaluation (cross validation, ROC curves, confusion matrices) - Team leadership - Collaboration with SMEs, software engineers, cloud developers - Experiment design and data simulation - Mission automation - Requirements definition - Transition to production - Staying current with emerging ML/pattern recognition algorithms But we must not invent details; these are all mentioned. However we need to ensure we don't duplicate content already in Responsibilities or Qualifications. If we list skills, we might repeat some content. But the instruction says use
    • for any list of duties/requirements/benefits. It didn't forbid listing skills as list. However we must preserve ALL substantive information; we can keep them in Responsibilities and Qualifications and not create a separate Skills section to avoid duplication. Simpler: just have sections: Responsibilities, Requirements, Qualifications, Benefits, Pay. We need to decide headings: "About the role" maybe not needed because we have Responsibilities. Could include a brief About the role paragraph summarizing role