System Data Analytics Specialist (Fraud / Callsign Optimization) at Capitex

, , United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

11 Jan, 26

Salary

0.0

Posted On

13 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Fraud Analytics, Digital Identity Systems, Callsign Optimization, SQL, Python, Data Visualization, Behavioral Biometrics, Device Fingerprinting, Event-Based Fraud Modeling, Analytical Mindset, Communication Skills, Machine Learning, Fraud Prevention, Risk Analytics, Authentication Systems, Regulatory Knowledge

Industry

Staffing and Recruiting

Description

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.

Responsibilities

The System Data Analytics Specialist will optimize and tune the Callsign fraud detection and authentication engine while analyzing data models and risk signals. They will collaborate with various teams to enhance fraud detection rules and ensure system efficacy.