Data Scientist Location: USA-VA-Arlington Job Description: Department:Government/Civilian Data Scientist General Information Requisition # 712 Security Clearance Required - IRS MBI Remote Type Hybrid Time Type Full time Description & Requirements Elder Research Inc., a wholly owned subsidiary of MANTECH international Corporation seeks a motivated, career and customer-oriented Data Scientist to join our team in Arlington, VA. This role is a remote role preferably in the Washington DC area. As a Data Scientist, you will support the Internal Revenue Service’s mission to improve tax compliance, fraud detection, and risk identification across large, complex tax and financial data environments. You will work directly with government clients, program managers, and technical teams to understand business and compliance challenges, design analytical approaches, and deliver data-driven solutions that inform enforcement, audit prioritization, and fraud prevention efforts. In this role, you will develop, test, and deploy predictive and statistical models using structured and unstructured data to identify anomalies, non-compliance risk, and potential fraud within tax records and related datasets. You will contribute across the full data science lifecycle, from problem formulation and data exploration through model validation, deployment, and stakeholder communication. Responsibilities include but are not limited to: - Prior programming experience, preferably in Python or R, including data exploration, feature engineering, model development, and writing modular, reusable, well-documented code within an iterative development process that includes peer review and collaboration - Demonstrated experience using Python, SQL, and Databricks for data analysis, modeling, statistical evaluation, and working with Markdown for technical documentation - Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data science–driven solutions in support of assessments, gap analyses, and actionable recommendations for IRS stakeholders - Design, develop, test, validate, and implement quantitative and qualitative data science solutions and predictive risk models (including audit selection, refund review, and fraud prevention initiatives) that are modular, maintainable, adaptable to evolving government and regulatory requirements, and supported by robustness, sensitivity, and significance testing to ensure defensible and explainable results - Apply statistical and machine learning techniques (supervised and unsupervised) to anomaly detection, fraud identification, and non-compliance risk scoring to help prioritize cases based on compliance risk, fraud indicators, and business impact - Collaborate with clients, subject matter experts, and cross-functional teams to refine problem statements, requirements, and analytical approaches, while demonstrating the ability to work independently in a collaborative, fast-paced environment - Prepare and deliver technical and non-technical briefings, reports, and presentations to audiences with varying levels of analytical sophistication, translating business and compliance needs into technical solutions with strong interpersonal, written, and verbal communication skills Minimum Qualifications: - Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences - 2-10+ years of experience in data science, analytics, or a related technical field - Experience using version control systems (e.g., Git) and collaborative development practices - Strong understanding of relational databases and SQL - Comfortable learning new tools, methodologies, and domains, including working outside your comfort zone - Strong analytical mindset with a willingness to tackle complex mathematical and statistical challenges - Willingness to travel and work on-site at client locations as required by project needs Preferred Qualifications: - Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields; experience applying advanced statistical concepts including sampling considerations, bias detection, weighting techniques, handling missing or outlier data, exploratory analysis, and longitudinal forecasting; and understanding of the data analytics lifecycle (e.g., CRISP-DM) - Experience with PySpark, Unity Catalog, and Jobs in Databricks, with familiarity using platforms and tools such as Databricks and AWS - Experience with Natural Language Processing (NLP) and text analytics applied to unstructured documents or case notes, as well as graph analytics and network analysis to identify relationships, fraud rings, or interconnected entities - Experience with containerization and environment management (e.g., venv, conda) - Experience operating in secure or remote government environments, including use of bash and command-line tools Clearance Requirements: - Must currently possess an IRS Public Trust clearance with Full Background Investigation Physical Requirements: - Must be able to remain in a stationary position 50% - Needs to occasionally move about inside the office to access file cabinets, office machinery, etc. - Frequently communicates with co-workers, management, and customers, which may involve delivering presentations. Must be able to exchange accurate information in these situation About Elder Research, Inc - People Centered. Data Driven Elder Research considers all qualified applicants for employment without regard to disability or veteran status or any other status protected under any federal, state, or local law or regulation.

# Data Scientist Location: Arlington United States Time Type Full time Job Description: General Information - Requisition #: 674 - Locations: USA-VA-Arlington - Posting Date: 03/04/2026 - Security Clearance Required: IRS MBI - Remote Type: Hybrid - Time Type: Full time Description & Requirements Elder Research Inc., a wholly owned subsidiary of MANTECH international Corporation seeks a motivated, career and customer-oriented Data Scientist to join our team in Arlington, VA. This role is a remote role preferably in the Washington DC area. As a Data Scientist, you will support the Internal Revenue Services mission to improve tax compliance, fraud detection, and risk identification across large, complex tax and financial data environments. You will work directly with government clients, program managers, and technical teams to understand business and compliance challenges, design analytical approaches, and deliver data-driven solutions that inform enforcement, audit prioritization, and fraud prevention efforts. In this role, you will develop, test, and deploy predictive and statistical models using structured and unstructured data to identify anomalies, non-compliance risk, and potential fraud within tax records and related datasets. You will contribute across the full data science lifecycle, from problem formulation and data exploration through model validation, deployment, and stakeholder communication. Responsibilities Responsibilities include but are not limited to: - Prior programming experience, preferably in Python or R, including data exploration, feature engineering, model development, and writing modular, reusable, well-documented code within an iterative development process that includes peer review and collaboration. - Demonstrated experience using Python, SQL, and Databricks for data analysis, modeling, statistical evaluation, and working with Markdown for technical documentation. - Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data science-driven solutions in support of assessments, gap analyses, and actionable recommendations for IRS stakeholders. - Design, develop, test, validate, and implement quantitative and qualitative data science solutions and predictive risk models (including audit selection, refund review, and fraud prevention initiatives) that are modular, maintainable, adaptable to evolving government and regulatory requirements, and supported by robustness, sensitivity, and significance testing to ensure defensible and explainable results. - Apply statistical and machine learning techniques (supervised and unsupervised) to anomaly detection, fraud identification, and non-compliance risk scoring to help prioritize cases based on compliance risk, fraud indicators, and business impact. - Collaborate with clients, subject matter experts, and cross-functional teams to refine problem statements, requirements, and analytical approaches, while demonstrating the ability to work independently in a collaborative, fast-paced environment. - Prepare and deliver technical and non-technical briefings, reports, and presentations to audiences with varying levels of analytical sophistication, translating business and compliance needs into technical solutions with strong interpersonal, written, and verbal communication skills. Minimum Qualifications - Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences. - 2-10+ years of experience in data science, analytics, or a related technical field. - Experience using version control systems (e.g., Git) and collaborative development practices. - Strong understanding of relational databases and SQL. - Comfortable learning new tools, methodologies, and domains, including working outside your comfort zone. - Strong analytical mindset with a willingness to tackle complex mathematical and statistical challenges. - Willingness to travel and work on-site at client locations as required by project needs. Preferred Qualifications - Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields. - Experience applying advanced statistical concepts including sampling considerations, bias detection, weighting techniques, handling missing or outlier data, exploratory analysis, and longitudinal forecasting. - Understanding of the data analytics lifecycle (e.g., CRISP-DM). - Experience with PySpark, Unity Catalog, and Jobs in Databricks, with familiarity using platforms and tools such as Databricks and AWS. - Experience with Natural Language Processing (NLP) and text analytics applied to unstructured documents or case notes. - Experience with graph analytics and network analysis to identify relationships, fraud rings, or interconnected entities. - Experience with containerization and environment management (e.g., venv, conda). - Experience operating in secure or remote government environments, including use of bash and command-line tools. Clearance Requirements - Must currently possess an IRS Public Trust clearance with Full Background Investigation. Physical Requirements - Must be able to remain in a stationary position 50%. - Needs to occasionally move about inside the office to access file cabinets, office machinery, etc. - Frequently communicates with co-workers, management, and customers, which may involve delivering presentations. - Must be able to exchange accurate information in these situations. About Elder Research, Inc People Centered. Data Driven Elder Research considers all qualified applicants for employment without regard to disability or veteran status or any other status protected under any federal, state, or local law or regulation.

Title: Senior Data Scientist Location: Arlington, Washington United States Job Description: Requisition # 717 Security Clearance Required - IRS MBI Remote Type Hybrid Time Type Full time Description & Requirements Elder Research Inc., a wholly owned subsidiary of MANTECH international Corporation seeks a motivated, career and customer-oriented Data Scientist to join our team in Arlington, VA. This role is a remote role preferably in the Washington DC area. As a Data Scientist, you will support the Internal Revenue Services mission to improve tax compliance, fraud detection, and risk identification across large, complex tax and financial data environments. You will work directly with government clients, program managers, and technical teams to understand business and compliance challenges, design analytical approaches, and deliver data-driven solutions that inform enforcement, audit prioritization, and fraud prevention efforts. In this role, you will develop, test, and deploy predictive and statistical models using structured and unstructured data to identify anomalies, non-compliance risk, and potential fraud within tax records and related datasets. You will contribute across the full data science lifecycle, from problem formulation and data exploration through model validation, deployment, and stakeholder communication. Responsibilities include but are not limited to: - Prior programming experience, preferably in Python or R, including data exploration, feature engineering, model development, and writing modular, reusable, well-documented code within an iterative development process that includes peer review and collaboration - Demonstrated experience using Python, SQL, and Databricks for data analysis, modeling, statistical evaluation, and working with Markdown for technical documentation - Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data sciencedriven solutions in support of assessments, gap analyses, and actionable recommendations for IRS stakeholders - Design, develop, test, validate, and implement quantitative and qualitative data science solutions and predictive risk models (including audit selection, refund review, and fraud prevention initiatives) that are modular, maintainable, adaptable to evolving government and regulatory requirements, and supported by robustness, sensitivity, and significance testing to ensure defensible and explainable results - Apply statistical and machine learning techniques (supervised and unsupervised) to anomaly detection, fraud identification, and non-compliance risk scoring to help prioritize cases based on compliance risk, fraud indicators, and business impact - Collaborate with clients, subject matter experts, and cross-functional teams to refine problem statements, requirements, and analytical approaches, while demonstrating the ability to work independently in a collaborative, fast-paced environment - Prepare and deliver technical and non-technical briefings, reports, and presentations to audiences with varying levels of analytical sophistication, translating business and compliance needs into technical solutions with strong interpersonal, written, and verbal communication skills Minimum Qualifications: - Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences - 5-10+ years of experience in data science, analytics, or a related technical field - Experience using version control systems (e.g., Git) and collaborative development practices - Strong understanding of relational databases and SQL - Comfortable learning new tools, methodologies, and domains, including working outside your comfort zone - Strong analytical mindset with a willingness to tackle complex mathematical and statistical challenges - Willingness to travel and work on-site at client locations as required by project needs Preferred Qualifications: - Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields; experience applying advanced statistical concepts including sampling considerations, bias detection, weighting techniques, handling missing or outlier data, exploratory analysis, and longitudinal forecasting; and understanding of the data analytics lifecycle (e.g., CRISP-DM) - Experience with PySpark, Unity Catalog, and Jobs in Databricks, with familiarity using platforms and tools such as Databricks and AWS</li> - Experience with Natural Language Processing (NLP) and text analytics applied to unstructured documents or case notes, as well as graph analytics and network analysis to identify relationships, fraud rings, or interconnected entities - Experience with containerization and environment management (e.g., venv, conda) - Experience operating in secure or remote government environments, including use of bash and command-line tools Clearance Requirements: - Must currently possess an IRS Public Trust clearance with Full Background Investigation Physical Requirements: - Must be able to remain in a stationary position 50% - Needs to occasionally move about inside the office to access file cabinets, office machinery, etc. - Frequently communicates with co-workers, management, and customers, which may involve delivering presentations. Must be able to exchange accurate information in these situation About Elder Research, Inc - People Centered. Data Driven Elder Research considers all qualified applicants for employment without regard to disability or veteran status or any other status protected under any federal, state, or local law or regulation.

Role Description As a Modeling Lead, you will work closely with the Head of Modeling to design, build, and operate Everly’s actuarial modeling ecosystem and the supporting actuarial/finance data foundation. This is a highly hands-on role spanning actuarial models, enterprise data design, and end-to-end data pipelines that power pricing, reserving, reporting, and analytics. You will also help leverage AI-enabled tooling to accelerate the development and evolution of Everly’s in-house actuarial modeling platform—improving development velocity, validation workflows, documentation, and insight generation while maintaining strong actuarial controls and governance. This position is ideal for a senior actuarial modeler who is comfortable operating at the intersection of actuarial science, software engineering, and modern data platforms. What You’ll Do - Actuarial Modeling & Execution - Build, enhance, and maintain actuarial models for pricing, reserving, projections, and financial analytics. - Translate modeling standards and approaches set by the Head of Modeling into robust, production-ready implementations. - Implement, test, and maintain actuarial assumptions, experience studies, and stress/scenario analyses. - Support recurring and ad-hoc model runs across development, validation, and production environments. - Refactor and optimize models for performance, transparency, and scalability. - Enterprise Data Model & Modern Data Pipeline Support - Help design and maintain the enterprise actuarial and finance data model, ensuring consistency across pricing, valuation, financial reporting, and analytics. - Partner with Data Engineering and Technology teams to support end-to-end data pipelines (ingestion → transformation → storage → consumption) that make actuarial outputs reliable and reusable. - Support data validation, reconciliation, and controls between source data, model inputs/outputs, and downstream reporting. - Contribute to a modern data lake/lakehouse approach where appropriate, and support use cases across platforms such as Snowflake and open table formats. - Modeling Infrastructure, Controls & Governance - Contribute to shared modeling frameworks, libraries, and reusable components. - Support model versioning, documentation, testing, and change management processes. - Assist with internal model reviews, validation efforts, audit requests, and regulatory support. - Identify model and data risks, limitations, and dependencies, and communicate them clearly. - AI-Enabled Modeling & Platform Evolution - Partner with the Head of Modeling to apply AI and automation to improve speed and quality of model development and enhancement. - Use AI-enabled tooling to support activities such as development acceleration, test generation, documentation drafting, model/result validation workflows, and scenario exploration—while keeping human actuarial judgment in control. - Evaluate new approaches pragmatically, prioritizing auditability, reproducibility, and measurable impact. - Cross-Functional Partnership - Work closely with Finance, Product, Data, Technology, and Operations teams to ensure actuarial models and data outputs are actionable and consumable. - Translate actuarial and financial results into clear insights for non-actuarial stakeholders. - Support strategic initiatives including new product development, reinsurance analysis, and platform evolution. Qualifications - Bachelor’s degree in Actuarial Science, Mathematics, Finance, Statistics, or a related field. - Progress toward, or attainment of, ASA or FSA (or equivalent designation). - 6–10+ years of experience in actuarial modeling within life insurance and/or annuities. - Strong hands-on experience in pricing and/or reserving models. - Strong quantitative development skills with proficiency in one or more of: Python, R, C++, Rust, Java. - Experience working with large datasets and structured analytical/modeling workflows. - Strong analytical skills with high attention to detail, controls, and data integrity. Benefits - Employees are eligible for an annual incentive bonus designed to reward for performance. - The salary range for this job in most geographic locations in the US is $161,000 to $176,000. - Flexible paid time off for PTO, plus paid holidays, days of Significance, and a Volunteer Day. - Paid parental leave eligible after 3 months of service. - Medical, Dental & Vision Insurance. - 401k with company match. - Profit Sharing & Savings Plan. - Short-term and long-term disability insurance. - Flexible spending account. - Life insurance. - Educational Assistance. - Associate Assistance Programs and more!