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Data & Machine Learning Engineer - Transaction Monitoring & AML

Omits Inc.

Remote · Nigeria Contract ₦600k – ₦600k/mo 4d ago

About the role

Company Description

Omits Financial & Technology Inc. is a licensed financial technology company specializing in secure cross-border payment solutions, multi-currency foreign exchange (FX) services, and digital wallet functionality.

We enable fast, safe, and transparent money transfers for individuals and businesses globally, supported by robust, automated KYC/AML compliance systems and a strong regulatory framework.

About the Role

We are building a next-generation cross-border payments platform and are seeking an engineer to design and implement our end-to-end transaction monitoring and financial crime detection system.

This is not a traditional machine learning role. You will be responsible for building a production-grade transaction monitoring engine that combines:

  • Rules-based detection (regulatory foundation)
  • Real-time risk scoring
  • Behavioral analytics and machine learning (layered on top)

The system will be:

  • Real-time and highly scalable
  • Fully explainable and audit-ready
  • Modular and capable of operating as a standalone platform

You will work closely with Engineering and Compliance to translate AML/CFT requirements into a robust, regulator-grade system.

Key Responsibilities

1. Transaction Monitoring Engine

  • Design and implement the core monitoring system for both real-time and batch transaction analysis
  • Develop a rules-based detection framework, including:
    • Velocity monitoring
    • Structuring detection
    • High-risk geography controls
    • New account and onboarding risk patterns
  • Ensure all rules are configurable, version-controlled, and fully auditable

2. Real-Time Risk Scoring

  • Build a real-time risk scoring engine that evaluates:
    • Customer profile
    • Transaction context
    • Behavioral patterns
  • Deliver sub-second decisioning to support transaction processing

3. Data & Feature Infrastructure

  • Design and implement pipelines for:
    • Event ingestion (transactions, customer actions, external signals)
    • Feature engineering (velocity metrics, behavioral aggregates)
  • Maintain both real-time and historical data layers for monitoring and analytics

4. Machine Learning (Augmentation Layer)

  • Develop models for:
    • Anomaly detection
    • Behavioral profiling
    • Risk signal enhancement
  • Focus on:
    • Explainability (feature importance, reason codes)
    • Reduction of false positives
  • Integrate ML on top of rules, not as a replacement

5. System Integration

  • Integrate the monitoring engine with:
    • Core transaction systems
    • Wallet and ledger infrastructure
    • KYC and sanctions screening systems
    • External risk and data providers
  • Ensure full data traceability and consistency across all components

6. Alerting & Case Support

  • Build alert generation logic based on defined risk thresholds
  • Structure outputs for compliance workflows, including:
    • Alert generation
    • Case investigation support
    • Regulatory reporting readiness (e.g., STR/SAR)

7. Monitoring & Optimization

  • Implement:
    • Rule performance monitoring
    • Model monitoring and drift detection
    • Alert quality tracking (false positives vs. true positives)
  • Continuously improve detection accuracy and operational efficiency

Required Qualifications

  • 3+ years experience in:
    • Backend engineering, data engineering, or machine learning
    • Experience in fintech, payments, fraud, or AML is strongly preferred
  • Strong proficiency in:
    • Python
    • SQL
    • Working with transactional and time-series data
  • Experience building:
    • Real-time or event-driven systems
    • Data pipelines and feature computation systems
  • Understanding of:
    • AML/CFT concepts (sanctions, PEP, structuring, velocity monitoring, etc.)
    • Risk-based transaction monitoring frameworks
  • Proven experience deploying systems in production environments

Nice to Have

  • Experience building fraud or AML monitoring systems
  • Familiarity with:
    • Kafka or event streaming systems
    • Feature stores
    • Graph/network analysis (entity linking, money movement patterns)
  • Experience working in regulated environments or supporting audits

What We’re Looking For

  • Strong systems thinker (not just model-focused)
  • Ability to translate compliance requirements into scalable technical systems
  • Attention to detail, especially around auditability and explanability
  • Ability to build modular, production-grade infrastructure from scratch

Why This Role Matters

You will be building a core piece of infrastructure that directly protects the platform, enables regulatory compliance, and supports long-term growth.

How to Apply

Please send your application to info@omitsfx.com and include details on:

  • Relevant projects or systems you’ve built
  • Links to GitHub or portfolio (if available)

Contract Type

3 months

Pay

₦600,000.00 per month

Work Location

Remote

Requirements

  • Python
  • SQL
  • Working with transactional and time-series data
  • Experience building: Real-time or event-driven systems, Data pipelines and feature computation systems
  • Understanding of: AML/CFT concepts (sanctions, PEP, structuring, velocity monitoring, etc.), Risk-based transaction monitoring frameworks
  • Proven experience deploying systems in production environments

Responsibilities

  • Design and implement the core monitoring system for both real-time and batch transaction analysis
  • Develop a rules-based detection framework, including: Velocity monitoring, Structuring detection, High-risk geography controls, New account and onboarding risk patterns
  • Ensure all rules are configurable, version-controlled, and fully auditable
  • Build a real-time risk scoring engine that evaluates: Customer profile, Transaction context, Behavioral patterns
  • Deliver sub-second decisioning to support transaction processing
  • Design and implement pipelines for: Event ingestion (transactions, customer actions, external signals), Feature engineering (velocity metrics, behavioral aggregates)
  • Maintain both real-time and historical data layers for monitoring and analytics
  • Develop models for: Anomaly detection, Behavioral profiling, Risk signal enhancement
  • Focus on: Explainability (feature importance, reason codes), Reduction of false positives
  • Integrate ML on top of rules, not as a replacement
  • Integrate the monitoring engine with: Core transaction systems, Wallet and ledger infrastructure, KYC and sanctions screening systems, External risk and data providers
  • Ensure full data traceability and consistency across all components
  • Build alert generation logic based on defined risk thresholds
  • Structure outputs for compliance workflows, including: Alert generation, Case investigation support, Regulatory reporting readiness (e.g., STR/SAR)
  • Implement: Rule performance monitoring, Model monitoring and drift detection, Alert quality tracking (false positives vs. true positives)
  • Continuously improve detection accuracy and operational efficiency

Skills

PythonSQL

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