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Log Analytics Engineer

  • Log Analytics Engineer job type Posted on: Oct 14, 2026
  • Log Analytics Engineer experience level Openkyber
  • Log Analytics Engineer employment type Atlanta, Georgia
  • Log Analytics Engineer employment type Onsite
  • Log Analytics Engineer salary Full-time

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Job Title :

Log Analytics Engineer

Job Type :

Full-Time

Job Location :

Atlanta Georgia United States

Remote :

No

Jobcon Logo Job Description :

Position: Data Scientist - Fraud Analytics

Location: Dallas, TX (Onsite)

Job Description:

Overall 8+ years of experience required

Looking for a Data Scientist with strong expertise in Fraud Analytics, Machine Learning, and AI-driven fraud detection solutions

You will lead end-to-end fraud use case discovery, model development, feature engineering, and deployment of advanced fraud detection systems leveraging ML, Graph Analytics, and AI techniques.

Identify predictive variables and build fraud feature catalogues.

You should build and validate pilot fraud detection models

Collaborate with engineering teams on low-latency scoring architectures.

Drive model explainability, governance, and compliance initiatives.

Support shadow runs and challenger model validation against existing systems.

Strong analytical and problem-solving skills, with a proven track record of identifying and resolving complex billing issues.

Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.

Looking forward for your reply.

For applications and inquiries,

Jobcon Logo Position Details

Posted:

Oct 14, 2026

Reference Number:

93233-38381

Employment:

Full-time

City:

Atlanta

Job Origin:

CIEPAL_ORGANIC_FEED

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Position: Data Scientist - Fraud Analytics

Location: Dallas, TX (Onsite)

Job Description:

Overall 8+ years of experience required

Looking for a Data Scientist with strong expertise in Fraud Analytics, Machine Learning, and AI-driven fraud detection solutions

You will lead end-to-end fraud use case discovery, model development, feature engineering, and deployment of advanced fraud detection systems leveraging ML, Graph Analytics, and AI techniques.

Identify predictive variables and build fraud feature catalogues.

You should build and validate pilot fraud detection models

Collaborate with engineering teams on low-latency scoring architectures.

Drive model explainability, governance, and compliance initiatives.

Support shadow runs and challenger model validation against existing systems.

Strong analytical and problem-solving skills, with a proven track record of identifying and resolving complex billing issues.

Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.

Looking forward for your reply.

For applications and inquiries,

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