System Data Analytics Specialist (Fraud / Callsign Optimization)

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Capitex is hiring a System Data Analytics Specialist focused on Fraud and Callsign optimization for a leading Saudi bank. This fully remote 6 month contract with potential extension requires hands on Callsign configuration, tuning, and analytics, plus data integration with core banking systems and dashboards to monitor efficacy. You’ll work with fraud strategy, risk and tech teams to reduce false positives while maintaining protection. Requirements include 5+ years in banking fraud or authentication, proven Callsign experience, strong SQL Python and data visualization skills, and English fluency (Arabic a plus). Apply with a resume highlighting measurable fraud reductions, tuning wins, and cross functional collaboration; be prepared to discuss thresholds, models, and integration examples.

Job Title: System Data Analytics Specialist (Fraud / Callsign Optimization)

Location: Remote (Global)
Contract Duration: 6 months (with possible extension)
Client: Leading Saudi Arabian Bank
Type: Contract / Consultant

About the Role

We are seeking a System Data Analytics Specialist with deep technical expertise in fraud analytics, digital identity systems, and Callsign platform optimization. The ideal candidate will have hands-on experience configuring, tuning, and optimizing Callsign or similar fraud detection and authentication systems within a banking or financial services environment.

You will play a key role in enhancing the accuracy, performance, and effectiveness of fraud detection rules and behavioral analytics. Working closely with fraud strategy, risk, and technology teams, you’ll ensure the Callsign system is delivering maximum protection with minimal customer friction.

Key Responsibilities

• System Optimization & Tuning
• Calibrate and fine-tune the Callsign fraud detection and authentication engine for optimal performance.
• Analyze data models, risk signals, and rule outputs to identify false positives/negatives and optimize model thresholds.
• Work on continuous improvement of Callsign workflows, journeys, and intervention logic.

• Data Analysis & Insights
• Conduct deep analytics on fraud event data, behavioral biometrics, and system logs.
• Build dashboards, performance metrics, and reporting pipelines to monitor Callsign efficacy.
• Collaborate with fraud analytics teams to detect trends and emerging threats.

• Technical Implementation
• Configure system integrations between Callsign and core banking systems, APIs, or orchestration layers.
• Support the deployment of new fraud scenarios and machine learning models.
• Manage test environments, perform regression testing, and validate production changes.

• Stakeholder Collaboration
• Liaise with fraud strategy, IT security, and vendor teams to drive performance improvements.
• Document configurations, model changes, and system updates.
• Provide expert guidance on Callsign data structures, risk scoring, and orchestration capabilities.

Required Skills & Experience

• Mandatory:
• Proven experience working directly with Callsign (configuration, tuning, or analytics).
• 5+ years of experience in fraud prevention, risk analytics, or authentication systems in the banking sector.
• Strong SQL, Python, and data visualization skills (e.g., Power BI, Tableau).
• Familiarity with behavioral biometrics, device fingerprinting, and event-based fraud modeling.
• Excellent analytical mindset and ability to interpret complex data flows.
• Strong communication skills in English (Arabic is a plus).

• Preferred:
• Experience with machine learning models used for fraud or risk scoring.
• Exposure to other fraud platforms (e.g., ThreatMetrix, Feedzai, SAS Fraud Management).
• Knowledge of regulatory environments in MENA banking and SAMA compliance standards.

What’s Offered

• Fully remote engagement – work from anywhere.
• 6-month contract, with possibility of renewal.
• Opportunity to work with a top-tier Saudi financial institution on a strategic fraud prevention initiative.
• Competitive daily rate, commensurate with experience.