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Senior Fraud Analyst

  • On-site
    • Tehran, Tehrān, Iran, Islamic Republic of
  • Commercial

Job description

About Snapp
Snapp is the pioneer provider of ride-hailing mobile solutions in Iran that connects smartphone owners in need of a ride to drivers who use their private cars offering transportation services. We are ambitious, passionate, engaged, and excited about pushing the boundaries of the transportation industry to new frontiers and be the first choice of each user in Iran.

About the position
We are seeking a Senior Fraud and Risk Specialist with expertise in Python, Machine Learning, and Data Engineering to design and implement cutting-edge fraud detection systems. Your mission will be to protect our organization from financial losses by leveraging AI-driven analytics, real-time monitoring, and predictive modeling while leading a small team of analysts.

Responsibilities

  • Data Engineering & Analytics:
    - Build ETL pipelines to process transaction data (Python, SQL).
    - Optimize fraud rule engines using behavioral analytics.

  • Fraud Detection & Prevention:
    - Develop and deploy ML models (e.g., anomaly detection, supervised classifiers) to identify fraudulent transactions.
    - Implement real-time risk scoring using streaming data.

  • Reporting & Compliance:
    - Generate risk assessment reports for stakeholders.

  • Team Leadership:
    - Mentor 1-2 junior analysts in statistical modeling and fraud investigation.
    - Collaborate with DevOps to deploy models in production (Docker).

Job requirements

Technical Skills:

  • Programming: Python (Pandas, Scikit-learn), SQL and Click-House.

  • Machine Learning: Fraud-specific models.

  • Data Engineering: ETL pipelines, Linux/Ubuntu, Big Data tools (Spark/Hadoop or ...).

  • Familiarity with DevOps (Docker, Git, AWS) pipelines is a plus.

  • Analytics Platforms.

  • Knowledge and experience in Linux/ubuntu servers is a requirement.

Soft Skills:

  • Leadership & team mentoring.

  • Strong numerical/statistical reasoning.

  • Ability to explain technical concepts to non-technical stakeholders.

Experience:

  • 5+ years in fraud analytics, risk modeling, or data science.

  • Proven track record of deploying ML models in production.

Education:

  • Bachelor’s/Master’s in Computer Science, Data Science, or Quantitative Finance.

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