Technical Financial Crime Manager at Paystack

As the Technical Financial Crime Manager, you will run the day-to-day fraud and AML detection stack; from data and rules to operational outcomes. You will combine deep technical expertise with financial crime domain knowledge to design effective monitoring systems, manage domain specialists, and ensure Paystack remains a safe, trusted payments platform.

You will be accountable for:

• The technical quality and effectiveness of fraud & AML monitoring logic
• The operating model and performance of Financial Crime Monitoring teams

Translating risk, regulatory, and business requirements into scalable detection systems

Key Responsibilities

Technical Ownership of Detection & Monitoring

• Define, build, test, and optimise fraud and AML detection rules, scenarios, thresholds, and models used in production systems.
• Translate complex datasets and domain insights into actionable detection logic embedded in monitoring and alerting platforms.
• Establish feedback loops between investigation outcomes and detection logic to continuously improve signal quality.
• Measure and manage detection performance using quantitative metrics (precision, recall, false positives, alert-to-case conversion, loss metrics).

Maintain structured, auditable documentation of rules, logic, assumptions, and changes.

Data Analysis, Modelling & Insights

• Analyse large, complex transactional and behavioural datasets to identify emerging fraud and AML risks across markets.
• Design and implement statistical models, machine learning approaches, and/or time-series analysis to enhance detection capabilities.
• Build and own dashboards and reporting frameworks tracking KPIs, SLAs, alert quality, investigator productivity, and risk outcomes.

Conduct trend analysis, root cause analysis, and deep dives on losses, typologies, and control gaps.

Financial Crime Oversight

• Own the end-to-end fraud and AML detection domain, ensuring alignment between prevention, detection, investigation, and remediation.
• Apply deep understanding of fraud typologies, AML/CTF risks, sanctions, and regulatory expectations to detection design.
• Manage the Fraud and AML operational teams (specialists and first-line managers) to ensure adequate coverage, capability and day-to-day execution. 
• Translate regulatory, partner, and audit requirements into scalable technical and operational controls.
• Stay ahead of evolving financial crime patterns, market-specific risks, and regulatory developments across Paystack’s footprint.

Tooling, Automation & Scale

• Partner with Product and Engineering to embed detection logic into core systems and improve monitoring, alerting, and case management tooling.
• Drive automation initiatives to reduce manual effort, improve consistency, and enable scale without compromising control quality.
• Identify and prioritise enhancements to monitoring platforms, workflows, and data pipelines.
• Ensure fraud and AML tooling evolves in line with transaction growth, new products, and new markets.

Operational Excellence

• Build and continuously improve operational processes, SLAs, KPIs, and quality frameworks across Fraud and AML teams.
• Use data and metrics to manage performance, capacity, and outcomes, ensuring teams operate efficiently and effectively.
• Identify gaps, risks, and inefficiencies, leading initiatives to strengthen controls and scale operations sustainably.
• Balance speed, quality, regulatory expectations, and customer experience in day-to-day decision-making.

Cross-Functional & Executive Collaboration

• Work closely with Product, Engineering, Data, Risk, Compliance, Legal, and Customer Operations. 
• Influence roadmap priorities related to fraud, AML, and financial crime tooling.
• Provide clear updates to senior stakeholders on operational performance, risks, and emerging issues

Support audits, partner reviews, and regulatory engagements as a subject matter expert.

Who We’re Looking For

Required 

• 7+ years in financial crime roles in payments, fintech, banking, or financial services.
• Strong technical expertise in data analysis, including advanced SQL and experience working with large, complex datasets.
• Expert Python skills, including experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, and/or model pipelines.
• Proven experience designing, building, and tuning risk detection systems (fraud, AML, or similar).
• Solid understanding of statistical modelling, machine learning, and/or time-series forecasting, with experience deploying models into production or operational workflows.
• Ability to translate data insights into operational detection logic used by investigators and automated systems.
• Experience measuring and optimising detection performance using quantitative metrics.

Strong systems thinking: able to design scalable, maintainable monitoring frameworks rather than one-off rules.

• Deep understanding of financial crime typologies, fraud patterns, AML/CTF requirements, and regulatory obligations.
• Experience operating within fraud, AML, risk, or compliance functions in payments, fintech, or financial services.
• Proven experience leading and developing teams, including setting direction, coaching, and performance management.
• Ability to balance technical depth with practical operational decision-making.
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

High ownership mindset and comfort operating in ambiguous, high-growth environments.

Preferred

• Experience with dbt and modern analytics stacks.
• Experience with version control systems (GitHub).
• Experience with AI-assisted tooling or advanced analytics platforms.
• Familiarity with monitoring platforms, alerting systems, transaction screening, and case management tools.
• Experience working with OLTP (MySQL/PostgreSQL/SQL Server), OLAP (Redshift/BigQuery/Snowflake), and NoSQL (MongoDB) databases.
• Industry certifications such as ACAMS, ICA, CFE, CFCS, or similar.