Data Scientist Level 3
IntelliGenesis LLC®
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Annapolis Junction, MD
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Mid-Senior level
Posted March 14, 2026
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Responsibilities
Commitments
Responsibilities
- Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)
- Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)
- Devise strategies for extracting meaning and value from large datasets
- Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge
- 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
- 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
- Effectively communicate complex technical information to non-technical audiences
- Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations
Commitments
US Citizens Only
Active TS/SCI Clearance and Polygraph required
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.
Not Met Priorities
What still needs stronger evidence
Requirements
- Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations
- US Citizens Only
- Active TS/SCI Clearance and Polygraph required
- Information Assurance Certification may be required
- Minimum of ten (10) years of relevant experience and a Bachelor’s degree or twelve (12) years of relevant experience and an Associate’s degree required.
- Relevant experience must be two of more of the following:
- Designing/implementing machine learning
- Data science
- Advanced analytical algorithms
- Programming (skill in at least one high-level language (e.g., Python))
- Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)
- Data management (e.g., data cleaning and transformation)
- Data mining
- Data modeling and assessment
- Artificial intelligence
Preferred Skills
- A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university
- Relevant experience must be two of more of the following:
- Designing/implementing machine learning
- Data science
- Advanced analytical algorithms
- Programming (skill in at least one high-level language (e.g., Python))
- Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)
- Data management (e.g., data cleaning and transformation)
- Data mining
- Data modeling and assessment
Education
- (Required) – Minimum of ten (10) years of relevant experience and a Bachelor’s degree or twelve (12) years of relevant experience and an Associate’s degree required.
- (Not required) – Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science
- (Not required) – A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university
Job Duties
Employ some combination (2 or more) of the following skill areas:
Foundations: (Mathematical, Computational, Statistical)
Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)
Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)
Devise strategies for extracting meaning and value from large datasets
Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge
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
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
Effectively communicate complex technical information to non-technical audiences
Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations
Required Skills:
US Citizens Only
Active TS/SCI Clearance and Polygraph required
Information Assurance Certification may be required
Minimum of ten (10) years of relevant experience and a Bachelor’s degree or twelve (12) years of relevant experience and an Associate’s degree required.
Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science
A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university
Relevant experience must be two of more of the following:
Designing/implementing machine learning
Data science
Advanced analytical algorithms
Programming (skill in at least one high-level language (e.g., Python))
Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)
Data management (e.g., data cleaning and transformation)
Data mining
Data modeling and assessment
Artificial intelligence
Software engineering Compensation Range: $68,146.36 - $149,921.99
_____________________________________________________________________________________________________
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.
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.
_____________________________________________________________________________________________________
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.
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.
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Employ some combination (2 or more) of the following skill areas:
Foundations: (Mathematical, Computational, Statistical)
Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility)
Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations)
Devise strategies for extracting meaning and value from large datasets
Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge
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
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
Effectively communicate complex technical information to non-technical audiences
Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations
Required Skills:
US Citizens Only
Active TS/SCI Clearance and Polygraph required
Information Assurance Certification may be required
Minimum of ten (10) years of relevant experience and a Bachelor’s degree or twelve (12) years of relevant experience and an Associate’s degree required.
Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science
A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university
Relevant experience must be two of more of the following:
Designing/implementing machine learning
Data science
Advanced analytical algorithms
Programming (skill in at least one high-level language (e.g., Python))
Statistical analysis (e.g., variability, sampling error, inference, hypothesis testing, EDA, application of linear models)
Data management (e.g., data cleaning and transformation)
Data mining
Data modeling and assessment
Artificial intelligence
Software engineering Compensation Range: $68,146.36 - $149,921.99
_____________________________________________________________________________________________________
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.
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.
_____________________________________________________________________________________________________
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.
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.
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