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Online Anomaly Detection Jobs (NOW HIRING)

Paramount

Senior Applied AI Engineer

Manhattan, NY · Hybrid

$115K - $157K/yr

Integrate AI into CI/CD pipelines for dynamic risk-based testing, anomaly detection, and ... o Vertex AI Online Endpoints for real-time inference Integrate Vertex AI with GCP services ...

Paramount

Senior Applied AI Engineer

Burbank, CA · Hybrid

$111K - $152K/yr

Integrate AI into CI/CD pipelines for dynamic risk-based testing, anomaly detection, and ... o Vertex AI Online Endpoints for real-time inference Integrate Vertex AI with GCP services ...

Paramount

Senior Applied AI Engineer

Manhattan, NY · On-site

$115K - $157K/yr

... anomaly detection). • Build, optimize, and tune RAG pipelines, including: o embedding and vector ... Vertex AI Online Endpoints for real-time inference • Integrate Vertex AI with GCP services ...

SHEIN

Senior Site Reliability Engineer

San Diego, CA

$60.50 - $80.50/hr

About SHEIN SHEIN is a global online fashion and lifestyle retailer, offering SHEIN branded apparel ... Triage and resolve production incidents, leveraging AI-assisted log analysis and anomaly detection ...

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Online Anomaly Detection information

What is the difference between Online Anomaly Detection vs Data Analyst?

AspectOnline Anomaly DetectionData Analyst
Required CredentialsBachelor's in CS, Data Science, or related fields; knowledge of machine learningBachelor's or higher in Statistics, Data Science, or related fields
Work EnvironmentTech companies, cybersecurity, finance, real-time data systemsBusiness, finance, marketing, reporting teams
Employer & Industry UsageUsed in industries requiring real-time monitoring and alertingUsed across various industries for data interpretation and reporting

Online Anomaly Detection focuses on real-time identification of unusual patterns in data streams, often requiring knowledge of machine learning and programming. Data Analysts interpret data, generate reports, and support decision-making. While both roles work with data, Online Anomaly Detection emphasizes automation and real-time analysis, whereas Data Analysts focus on historical data analysis and insights.

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Online Anomaly Detection jobs near you
Infographic showing various Online Anomaly Detection job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 62% Full Time, 34% Part Time, 1% Temporary, and 2% Contract. Highlights an 82% Physical, 1% Hybrid, and 17% Remote job distribution.

Senior Data Scientist - Digital Intelligence, Device Signals

Socure

San Francisco, CA • On-site

$150K - $185K/yr

Full-time

Re-posted 11 days ago

Job description

Why Socure?
Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading.
About the Role
Socure is the leading provider of digital identity verification and fraud prevention solutions, leveraging AI and machine learning to power the most accurate identity trust decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.
We are seeking a Senior Data Scientist to join our Digital Intelligence team. In this role, you will drive the development of machine learning features and models that leverage device, network, and behavioral data to power fraud prevention and identity verification. You'll work with rich, high-volume data from browser, mobile, and API traffic to surface meaningful insights and scalable risk signals. This is a great opportunity to own impactful projects, collaborate cross-functionally, and deepen your expertise in applied ML for device and behavioral intelligence.
What You'll Do
  • Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention-balancing precision, recall, and real-world adversarial dynamics.
  • Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data).
  • Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse.
  • Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques.
  • Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning.
  • Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust.
  • Contribute to team standards for ML explainability, risk evaluation, and feature logging.
  • Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences.
  • Mentor junior data scientists and participate in cross-functional working groups.

What You Bring
  • Master's degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
  • 6+ years of experience in data science or applied machine learning, including experience working in production environments.
  • Excellent SQL skills and extensive experience with large-scale databases and data modeling.
  • Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data.
  • Proficiency in Python and distributed computing tools (e.g., Spark, PySpark).
  • Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or similar.
  • Excellent communication skills-able to explain complex technical results to non-technical stakeholders and senior leadership.
  • Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness.
  • Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies.
  • Strong judgment across data quality, model selection, and business impact tradeoffs.
  • Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams.
Preferred Qualifications
  • Background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling.
  • Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings).
  • Familiarity with privacy-preserving or robust ML techniques.
  • Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing.
What You'll Gain
  • Hands-on experience with real-world data science challenges in a high-impact industry.
  • A collaborative and inclusive work environment that fosters learning and growth.
  • Opportunities to grow into staff-level or technical leadership roles over time.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly.
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