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Graph Anomaly Detection Jobs in Ohio (NOW HIRING)

Bread Financial

Senior Staff Agentic AI Engineer

Columbus, OH · On-site +1

$102K - $139K/yr

... anomaly detection, and decisioning. The role includes implementing deterministic paths where needed, integrating graph/RAG memory, and establishing strong observability and evaluation pipelines. You ...

JP Morgan Chase

Senior Lead AI Security Engineer

Columbus, OH · On-site

$107K - $147K/yr

Graph ML for identity/threat detection; anomaly detection over telemetry. * GPU optimization, model quantization/distillation, and on-prem/private model deployment. * Familiarity with governance for ...

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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 Ohio?

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

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

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

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

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

Graph Anomaly Detection jobs near you

Senior Lead Security Engineer, AI

Columbus, OH • On-site

JPMorgan Chase & Co.
Finance and Insurance • 10K+ employees
Learn more about JPMorgan Chase & Co.external

$107K - $146K/yr

Other

Posted 13 days ago

Key responsibilities

  • Lead end-to-end design and delivery of AI solutions for cyber use cases, including problem framing, data integration, model development, evaluation, deployment, and monitoring.

  • Build secure LLM/RAG services and ML pipelines that integrate with various security and IT systems such as SIEM, XDR, EDR, SOAR, IAM, ITSM, CMDB, code repositories, and cloud telemetry.

  • Establish engineering standards for secure AI, including prompt security, input/output validation, PII masking, secrets handling, and deterministic fallbacks.

JPMorgan Chase & Co. rating

7.9

Company rating: 7.9 out of 10

Based on 500 frontline employees who took The Breakroom Quiz

Job description

Job Summary

As a Senior Lead AI Security Engineer in our Cybersecurity team, you will design and deliver secure artificial intelligence solutions that support critical cyber use cases. You will play a key role in shaping platform standards and governance, collaborating with cross-functional teams, and driving innovation in secure AI. Together, we will build foundational capabilities and create lasting impact for our organization and the wider community.

Job responsibilities
  • Lead end-to-end design and delivery of AI solutions for cyber use cases, from problem framing and data integration to model development, evaluation, deployment, and monitoring.
  • Build secure LLM/RAG services and ML pipelines that integrate with SIEM/XDR, EDR, SOAR, IAM, ITSM, CMDB, code repos, and cloud telemetry.
  • Establish engineering standards for secure AI: prompt security, tool/function calling patterns, input/output validation, PII masking, secrets handling, and deterministic fallbacks.
  • Create evaluation harnesses with offline/online metrics, golden datasets, adversarial prompt sets, jailbreak tests, and safety/quality KPIs.
  • Partner with platform teams to stand up reusable AI components: LLM gateways, vector stores, feature stores, evaluation/observability, and governance workflows.
  • Implement drift and quality monitoring; define SLAs/SLOs; build incident response runbooks for AI-enabled services.
  • Collaborate with risk and MRGR-style governance partners to meet documentation, validation, and attestations; maintain model/AT inventories, monitoring plans, and change logs.
  • Deliver measurable impact: reduce MTTR, improve detection precision, automate control evidence collection, and accelerate secure engineering.
  • Mentor engineers and analysts; publish playbooks, templates, and safe prompt libraries; lead brown-bags and office hours for adoption.
  • Drive a roadmap of 2–3 flagship capabilities per year (e.g., SOC triage assistant, controls automation agent, DevSecOps code copilot).
Required qualifications, capabilities and skills
  • Minimum 7 years of software/security engineering, including hands‑on experience in one or more of: detection engineering, SecOps, AppSec/DevSecOps, or cloud security.
  • Minimum 3 years building and operating applied ML/LLM systems in production (RAG pipelines, embeddings, fine‑tuning/specialization, vector databases, model serving).
  • Proficiency in Python and at least one of: Java, Scala, or TypeScript; experience with microservices, APIs, containers, and Kubernetes.
  • Familiarity with SIEM, EDR, SOAR, IAM, and ITSM integrations; streaming/data engineering with Kafka or similar.
  • Experience with LLM orchestration and guardrails (prompt engineering, injection defense, tool calling, safety filters).
  • Hands‑on with ML/LLM ecosystems: PyTorch or TensorFlow; scikit‑learn; LangChain/LlamaIndex; ONNX/Triton/Ray
  • Strong understanding of secure SDLC, privacy, and data protection; ability to partner with governance to meet documentation and monitoring requirements.
  • Demonstrated ability to ship secure, reliable AI features with clear metrics and post‑deployment monitoring.
Preferred qualifications, capabilities and skills
  • Experience building developer copilots for AppSec/DevSecOps (IaC scanning, secrets detection, SAST/DAST triage).
  • Cloud security engineering across one or more major providers; IaC and policy‑as‑code.
  • Experience or exposure to Cyber operations, Adversarial ML and LLM red teaming experience (prompt injection, data exfiltration, model abuse, poisoning defenses).
  • Graph ML for identity/threat detection; anomaly detection over telemetry.
  • GPU optimization, model quantization/distillation, and on‑prem/private model deployment.
  • Familiarity with governance for AI/ML systems in regulated environments.

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