ZipRecruiter

Log In

1

Graph Anomaly Detection Jobs in Virginia (NOW HIRING)

Elder Research

Data Scientist

Arlington, VA · On-site

... to anomaly detection, fraud identification, and non-compliance risk scoring to help prioritize ... well as graph analytics and network analysis to identify relationships, fraud rings, or ...

Elder Research Inc.

Senior Data Scientist

Arlington, VA · On-site

$90 - $120/hr

... to anomaly detection, fraud identification, and non-compliance risk scoring to help prioritize ... well as graph analytics and network analysis to identify relationships, fraud rings, or ...

Elder Research

Data Scientist

Arlington, VA · On-site

... to anomaly detection, fraud identification, and non-compliance risk scoring to help prioritize ... well as graph analytics and network analysis to identify relationships, fraud rings, or ...

Wiser

Data Scientist (Mid) - TS/SCI

Springfield, VA · On-site

$85K - $120K/yr

... graph schemas, ontologies, OWL, and data base inferencing. Experience with combining digital ... Integrity / Security, Data Anomaly Detection, Statistical Analysis, S-57 data structure ...

Thomson Reuters

Data Scientist

Mclean, VA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Large Data Analysis: statistical methods, graph networks, anomaly detection, spatial analysis, pattern-of-life, sentiment analysis. * Large Data Modeling: risk modeling, community detection ...

THOMSON REUTERS

Data Scientist

Mclean, VA · Hybrid

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Large Data Analysis: statistical methods, graph networks, anomaly detection, spatial analysis, pattern-of-life, sentiment analysis. * Large Data Modeling: risk modeling, community detection ...

previous page

Showing results 41-60

All JobsGraph Anomaly Detection JobsGraph Anomaly Detection Jobs in Virginia

Graph Anomaly Detection information

What is graph anomaly detection?

Graph anomaly detection is the process of identifying unusual patterns, nodes, edges, or substructures within graph data that deviate from expected behavior. These anomalies could indicate fraud, security breaches, network failures, or other significant events in applications such as cybersecurity, social networks, and financial transactions. Techniques in graph anomaly detection leverage statistical, machine learning, and deep learning approaches to analyze complex relationships and structures within graph data. Detecting these anomalies helps organizations quickly address potential threats or irregularities. The field is interdisciplinary, combining knowledge from data science, graph theory, and domain-specific expertise.

What are the key skills and qualifications needed to thrive in graph anomaly detection?

To excel in Graph Anomaly Detection, you need strong skills in data science, graph theory, and machine learning, typically supported by a degree in computer science or a related field. Proficiency with Python, libraries like NetworkX or PyTorch Geometric, and familiarity with graph databases such as Neo4j are commonly required. Analytical thinking, problem-solving abilities, and attention to detail help professionals identify subtle anomalies and communicate findings effectively. These skills ensure accurate detection of unusual patterns, supporting cybersecurity, fraud prevention, and other critical applications.

What are common challenges faced by professionals working in graph anomaly detection, and how can they be addressed?

Professionals in Graph Anomaly Detection often face challenges such as handling large-scale, high-dimensional graph data and distinguishing between legitimate anomalies and noise. Collaborating closely with data engineers and domain experts is essential to ensure accurate labeling and effective feature engineering. Additionally, staying updated with rapidly evolving algorithms and tools is important for optimizing detection performance. Regular cross-functional meetings and ongoing training can help address these challenges and support continuous improvement.

What is the difference between Graph Anomaly Detection vs Data Scientist?

AspectGraph Anomaly DetectionData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of graph theory and machine learningDegree in Statistics, Computer Science, or related fields; proficiency in programming and data analysis
Work EnvironmentResearch labs, tech companies, industries analyzing network dataBusiness, finance, tech firms analyzing large datasets for insights
Industry UsageSpecialized in detecting irregularities in graph-structured dataBroadly used for data analysis, predictive modeling, and decision-making

While both roles involve data analysis and machine learning, Graph Anomaly Detection focuses specifically on identifying irregularities within graph-structured data, whereas Data Scientists work across various data types and analytical tasks. Understanding these differences helps organizations choose the right expertise for their data challenges.

What are popular job titles related to Graph Anomaly Detection jobs in Virginia?

For Graph Anomaly Detection jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Graph Anomaly Detection jobs in Virginia look for?

The top searched job categories for Graph Anomaly Detection jobs in Virginia are:

What cities in Virginia are hiring for Graph Anomaly Detection jobs?

Cities in Virginia with the most Graph Anomaly Detection job openings:

Graph Anomaly Detection jobs near you
Infographic showing various Graph Anomaly Detection job openings in Virginia as of August 2026, with employment types broken down into 85% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Data Scientist

Elder Research

Arlington, VA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Data Scientist
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 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.
If you need a reasonable accommodation to apply for a position with Elder Research, please email us at and provide your name and contact information.

Apply