Technical Financial Crime Manager at Paystack

What You’ll Do

• 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.