Data Scientist Level 3

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  "active_status": "active"
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    "raw": "Annapolis Junction, MD, 20701",
    "city": "Annapolis Junction",
    "region": "MD",
    "country": "United States",
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{
  "detail": {
    "url": "https://intelligenesis.applytojob.com/apply/jobs/details/YPVkglZm4L?&",
    "heading": "Data Scientist Level 3",
    "html_title": "JazzHR » Job Listings",
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    "description_html": "<p><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><b><u><span style=\"color:rgb(38,38,38);\">Job Duties</span></u></b></span></span></p><ul><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Employ some combination (2 or more) of the following skill areas:</span></span><ul style=\"list-style-type:circle;\"><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Foundations: (Mathematical, Computational, Statistical)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)</span></span></li></ul></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Devise strategies for extracting meaning and value from large datasets</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Through analytic modeling, statistical analysis, programming, and/or other appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in customer data holdings</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist other with drawing appropriate conclusions from the analysis of such data</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Effectively communicate complex technical information to non-technical audiences</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations</span></span></li></ul><p><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><b><u><span style=\"color:rgb(38,38,38);\">Required Skills:</span></u></b></span></span></p><ul><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">US Citizens Only</span></span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">Active TS/SCI Clearance and Polygraph required</span></span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">Information Assurance Certification may be required </span></span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">Minimum of eight (8) years of relevant experience and a Master's degree; ten (10) </span>years of relevant experience and a Bachelor’s degree or 12 years of relevant experience and an Associate’s degree required.  </span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Relevant experience must be two of more of the following:</span></span><ul style=\"list-style-type:circle;\"><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Designing/implementing machine learning</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data science</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Advanced analytical algorithms</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Programming (skill in at least one high-level language (e.g., Python))</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data management (e.g., data cleaning and transformation)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data mining</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data modeling and assessment</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Artificial intelligence</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Software engineering</span></span></li></ul></li></ul><div style=\"margin-left:8px;\"><br><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><strong><u>Compensation Range:</u> $109,000 - $149,000</strong></span></span></div><p></p><p>_____________________________________________________________________________________________________</p>\n\n<p><em><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">Compensation ranges encompass a total compensation package and are a general guideline only and not intended as a guaranteed and/or implied final compensation or salary for this job opening. Determination of official compensation or salary relies on several different factors including, but not limited to: level of position, complexity of job responsibilities, geographic location, candidate’s scope of relevant work experience, educational background, certifications, contract-specific affordability, organizational requirements and alignment with local market data. </span></span></em></p>\n\n<p><em><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">Our compensation includes other indirect financial components designed to support employees’ total well-being, which should be considered when evaluating our competitive benefits package. These monetary benefits include medical insurance, life insurance, disability, paid time off, maternity/paternity leave, 401(k) company match, training/education reimbursements and other work/life programs.</span></span></em></p>\n\n<p>_____________________________________________________________________________________________________</p>\n\n<p><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">IntelliGenesis is committed to providing equal opportunity to all employees and applicants for employment. The Company is an Equal Opportunity Employer (EOE), and as such, does not tolerate discrimination, retaliation, or harassment of its employees or applicants based upon race, color, religion, gender, sexual orientation, national origin, age, genetic information, disability, or any other protected characteristic under local, state, or federal law in any employment practice. Such employment practices include, but are not limited to: hiring, promotion, demotion, transfer, recruitment, or recruitment advertising, selection, disciplinary action layoff, termination, rates of pay, or other forms of compensation and selection of training. </span></span></p>\n\n<p><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">IntelliGenesis is committed to the fair and equal employment of individuals with disabilities. It is the Company’s policy to reasonably accommodate qualified individuals with disabilities unless the accommodation would impose an undue hardship on the organization. In accordance with the Americans with Disabilities Act (ADA) as amended, reasonable accommodations will be provided to qualified individuals with disabilities, when such accommodations are necessary, to enable them to perform the essential functions of their jobs or to enjoy the equal benefits and privileges of employment. This policy applies to all applicants for employment and all employees.</span></span></p>",
    "description_text": "Job Duties\n Employ some combination (2 or more) of the following skill areas: Foundations: (Mathematical, Computational, Statistical)\n Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)\n Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)\n Devise strategies for extracting meaning and value from large datasets\n Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge\n Through analytic modeling, statistical analysis, programming, and/or other appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in customer data holdings\n Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist other with drawing appropriate conclusions from the analysis of such data\n Effectively communicate complex technical information to non-technical audiences\n Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations\n Required Skills:\n US Citizens Only\n Active TS/SCI Clearance and Polygraph required\n Information Assurance Certification may be required\n Minimum of eight (8) years of relevant experience and a Master's degree; ten (10)  years of relevant experience and a Bachelor’s degree or 12 years of relevant experience and an Associate’s degree required.\n Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science\n A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university\n Relevant experience must be two of more of the following: Designing/implementing machine learning\n Data science\n Advanced analytical algorithms\n Programming (skill in at least one high-level language (e.g., Python))\n Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)\n Data management (e.g., data cleaning and transformation)\n Data mining\n Data modeling and assessment\n Artificial intelligence\n Software engineering\n Compensation Range: $109,000 - $149,000\n _____________________________________________________________________________________________________\n Compensation ranges encompass a total compensation package and are a general guideline only and not intended as a guaranteed and/or implied final compensation or salary for this job opening. Determination of official compensation or salary relies on several different factors including, but not limited to: level of position, complexity of job responsibilities, geographic location, candidate’s scope of relevant work experience, educational background, certifications, contract-specific affordability, organizational requirements and alignment with local market data.\n Our compensation includes other indirect financial components designed to support employees’ total well-being, which should be considered when evaluating our competitive benefits package. These monetary benefits include medical insurance, life insurance, disability, paid time off, maternity/paternity leave, 401(k) company match, training/education reimbursements and other work/life programs.\n _____________________________________________________________________________________________________\n IntelliGenesis is committed to providing equal opportunity to all employees and applicants for employment. The Company is an Equal Opportunity Employer (EOE), and as such, does not tolerate discrimination, retaliation, or harassment of its employees or applicants based upon race, color, religion, gender, sexual orientation, national origin, age, genetic information, disability, or any other protected characteristic under local, state, or federal law in any employment practice. Such employment practices include, but are not limited to: hiring, promotion, demotion, transfer, recruitment, or recruitment advertising, selection, disciplinary action layoff, termination, rates of pay, or other forms of compensation and selection of training.\n IntelliGenesis is committed to the fair and equal employment of individuals with disabilities. It is the Company’s policy to reasonably accommodate qualified individuals with disabilities unless the accommodation would impose an undue hardship on the organization. In accordance with the Americans with Disabilities Act (ADA) as amended, reasonable accommodations will be provided to qualified individuals with disabilities, when such accommodations are necessary, to enable them to perform the essential functions of their jobs or to enjoy the equal benefits and privileges of employment. This policy applies to all applicants for employment and all employees.",
    "jsonld_jobposting": {
      "url": "https://careers.intelligenesisllc.com/apply/YPVkglZm4L/Data-Scientist-Level-3",
      "@type": "JobPosting",
      "title": "Data Scientist Level 3",
      "@context": "http://schema.org/",
      "datePosted": "2026-03-09",
      "description": "<p><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><b><u><span style=\"color:rgb(38,38,38);\">Job Duties</span></u></b></span></span></p><ul><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Employ some combination (2 or more) of the following skill areas:</span></span><ul style=\"list-style-type:circle;\"><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Foundations: (Mathematical, Computational, Statistical)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)</span></span></li></ul></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Devise strategies for extracting meaning and value from large datasets</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Through analytic modeling, statistical analysis, programming, and/or other appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique features and limitations inherent in customer data holdings</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist other with drawing appropriate conclusions from the analysis of such data</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Effectively communicate complex technical information to non-technical audiences</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations</span></span></li></ul><p><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><b><u><span style=\"color:rgb(38,38,38);\">Required Skills:</span></u></b></span></span></p><ul><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">US Citizens Only</span></span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">Active TS/SCI Clearance and Polygraph required</span></span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">Information Assurance Certification may be required </span></span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><span style=\"color:rgb(38,38,38);\">Minimum of eight (8) years of relevant experience and a Master's degree; ten (10) </span>years of relevant experience and a Bachelor’s degree or 12 years of relevant experience and an Associate’s degree required.  </span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Relevant experience must be two of more of the following:</span></span><ul style=\"list-style-type:circle;\"><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Designing/implementing machine learning</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data science</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Advanced analytical algorithms</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Programming (skill in at least one high-level language (e.g., Python))</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data management (e.g., data cleaning and transformation)</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data mining</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Data modeling and assessment</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Artificial intelligence</span></span></li><li style=\"margin-left:8px;\"><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\">Software engineering</span></span></li></ul></li></ul><div style=\"margin-left:8px;\"><br><span style=\"font-size:16px;\"><span style=\"font-family:'Times New Roman', Times, serif;\"><strong><u>Compensation Range:</u> $109,000 - $149,000</strong></span></span></div><p></p><p>_____________________________________________________________________________________________________</p>\n\n<p><em><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">Compensation ranges encompass a total compensation package and are a general guideline only and not intended as a guaranteed and/or implied final compensation or salary for this job opening. Determination of official compensation or salary relies on several different factors including, but not limited to: level of position, complexity of job responsibilities, geographic location, candidate’s scope of relevant work experience, educational background, certifications, contract-specific affordability, organizational requirements and alignment with local market data. </span></span></em></p>\n\n<p><em><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">Our compensation includes other indirect financial components designed to support employees’ total well-being, which should be considered when evaluating our competitive benefits package. These monetary benefits include medical insurance, life insurance, disability, paid time off, maternity/paternity leave, 401(k) company match, training/education reimbursements and other work/life programs.</span></span></em></p>\n\n<p>_____________________________________________________________________________________________________</p>\n\n<p><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">IntelliGenesis is committed to providing equal opportunity to all employees and applicants for employment. The Company is an Equal Opportunity Employer (EOE), and as such, does not tolerate discrimination, retaliation, or harassment of its employees or applicants based upon race, color, religion, gender, sexual orientation, national origin, age, genetic information, disability, or any other protected characteristic under local, state, or federal law in any employment practice. Such employment practices include, but are not limited to: hiring, promotion, demotion, transfer, recruitment, or recruitment advertising, selection, disciplinary action layoff, termination, rates of pay, or other forms of compensation and selection of training. </span></span></p>\n\n<p><span style=\\\"font-size:14px\\\"><span style=\\\"font-family:Times New Roman,Times,serif\\\">IntelliGenesis is committed to the fair and equal employment of individuals with disabilities. It is the Company’s policy to reasonably accommodate qualified individuals with disabilities unless the accommodation would impose an undue hardship on the organization. In accordance with the Americans with Disabilities Act (ADA) as amended, reasonable accommodations will be provided to qualified individuals with disabilities, when such accommodations are necessary, to enable them to perform the essential functions of their jobs or to enjoy the equal benefits and privileges of employment. This policy applies to all applicants for employment and all employees.</span></span></p>",
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