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Anomaly Detection Jobs (NOW HIRING)

Software Technology Inc

ML Engineer

Pensacola, FL

Design and implement robust anomaly detection algorithms using statistical, machine learning, and deep learning techniques. * Develop scalable data pipelines to process and analyze large volumes of ...

True Anomaly

Machine Learning Engineer (I-III)

Denver, CO · On-site

$105K - $220K/yr

Working alongside experienced engineers, you will support the development of models and pipelines that enable object classification, anomaly detection, and data-driven decision-making. You are ...

Appgate

Staff Machine Learning Engineer

Manhattan, NY · On-site

$180K - $220K/yr

Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis. * Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model ...

Appgate

Principal Machine Learning Engineer

Manhattan, NY · On-site

$220K - $265K/yr

Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis. * Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model ...

Walmart

Principal, Data Scientist

Rogers, AR · On-site

$110K - $220K/yr

Develop intelligent anomaly detection frameworks leveraging techniques such as Isolation Forest, Random Forest, statistical methods, and unsupervised learning algorithms. * Partner with business ...

Walmart

Principal, Data Scientist

Cave Springs, AR

$110K - $220K/yr

Develop intelligent anomaly detection frameworks leveraging techniques such as Isolation Forest, Random Forest, statistical methods, and unsupervised learning algorithms. * Partner with business ...

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How much do anomaly detection jobs pay per hour?

As of Jun 21, 2026, the average hourly pay for anomaly detection in the United States is $38.48, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $51.68 per hour, depending on experience, location, and employer.

What are typical daily tasks and responsibilities for someone working in Anomaly Detection?

Professionals in Anomaly Detection typically spend their days analyzing large datasets to identify unusual patterns or behaviors that could indicate errors, fraud, or other significant events. They build and maintain models using statistical techniques and machine learning algorithms, validate detected anomalies, and collaborate closely with data engineers, cybersecurity teams, or business analysts depending on the industry. Regular reporting of findings, tuning detection systems for accuracy, and staying updated with emerging methodologies are also important aspects of the job. The role often requires working both independently and as part of a multidisciplinary team to ensure timely and actionable insights are delivered.

What are the key skills and qualifications needed to thrive in the Anomaly Detection position, and why are they important?

To thrive in an Anomaly Detection role, you need a strong background in data analysis, statistics, and machine learning, often supported by a degree in computer science, mathematics, or a related field. Familiarity with programming languages like Python or R, and experience using data analysis tools such as TensorFlow, Scikit-learn, or specialized anomaly detection frameworks, are typically required. Strong problem-solving skills, attention to detail, and effective communication enhance your ability to interpret findings and share insights with cross-functional teams. These skills are essential for accurately identifying unusual patterns in data and contributing to an organization's data-driven decision-making processes.

What is an Anomaly Detection job?

An Anomaly Detection job involves identifying unusual patterns or deviations in data that do not conform to expected behavior. Professionals in this role use statistical methods, machine learning, and AI techniques to detect fraudulent activities, network intrusions, or system failures. They work in various industries such as finance, cybersecurity, healthcare, and manufacturing. Responsibilities may include data preprocessing, model training, and real-time anomaly detection to improve security and operational efficiency.

More about Anomaly Detection jobs
What are the most commonly searched types of Anomaly Detection jobs? The most popular types of Anomaly Detection jobs are:
What states have the most Anomaly Detection jobs? States with the most job openings for Anomaly Detection jobs include:
What job categories do people searching Anomaly Detection jobs look for? The top searched job categories for Anomaly Detection jobs are:
Anomaly Detection jobs near you
Infographic showing various Anomaly Detection job openings in the United States as of June 2026, with employment types broken down into 4% As Needed, 79% Full Time, 1% Part Time, and 16% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $80,032 per year, or $38.5 per hour.
ML Engineer

ML Engineer

Software Technology Inc

Pensacola, FL

Other

Posted 14 days ago

Job description

ML Engineer

Location: CA, WA, FL, NY (Disney Hubs) – Onsite

Orlando, Florida

San Francisco, California

Seattle, Washington

New York, New York

Skills: Machine learning, and deep learning techniques, exploratory data analysis (EDA), data science

Key Responsibilities:
  • Design and implement robust anomaly detection algorithms using statistical, machine learning, and deep learning techniques.
  • Develop scalable data pipelines to process and analyze large volumes of streaming and batch data.
  • Collaborate with cross-functional teams (engineering, data science, product) to define problem statements, gather requirements, and deliver ML solutions.
  • Perform exploratory data analysis (EDA) and feature engineering tailored for anomaly detection.
  • Train, evaluate, and optimize models to maximize detection accuracy and minimize false positives/negatives.
  • Monitor model performance and update models as needed in production environments.
  • Write clean, maintainable, and well-documented code following best practices.
  • Stay up-to-date with the latest research and technologies in anomaly detection and machine learning.
  • Assist in developing dashboards and tools for anomaly visualization and alerting.

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