Who we are

Moniepoint Inc. is Africa’s all-in-one financial ecosystem, helping 10 million businesses and individuals access seamless payments, banking, credit, and business management tools since 2019.

As Nigeria’s largest merchant acquirer, it powers most of the country’s Point of Sale (POS) transactions. Through its subsidiaries, Moniepoint Inc. processes $22 billion monthly for its customers while operating profitably.

About the role : 

At Moniepoint, data is at the core of everything we do. We are a customer-centric company, and your work will enable our teams to make informed, data-driven decisions that directly impact the success of our business. We’re looking for an experienced Senior Data Scientist who’s excited to solve meaningful problems, collaborate across teams, and turn complex data into clear, actionable insights. You’ll work at the intersection of data, product, and engineering to uncover insights, guide strategic decisions, and develop models that enhance user experience and business outcomes.

Curious about what makes Moniepoint an incredible place to work? Check out posts on how we cultivate a culture of innovation, teamwork, and growth.

What You’ll Do

• Lead high-impact projects: Design and deliver end-to-end data science solutions that support product innovation and business strategy.
• Uncover insights: Analyze large, complex datasets to identify trends, surface opportunities, and influence key decisions.
• Build models: Develop and deploy predictive and prescriptive models using machine learning and statistical techniques.
• Enable experimentation: Design A/B tests and causal inference studies to help teams learn quickly and make informed choices.
• Collaborate cross-functionally: Work closely with product managers, engineers, and business leaders to understand goals and deliver data-driven solutions.
• Promote data fluency: Build dashboards, tools, and frameworks to enable self-service analytics and scale your impact across teams.

To succeed in this role, you should have

• 5+ years of experience as a Data Scientist, ideally in fast-paced or high-growth environments
• Proficiency in SQL and experience working with large-scale data systems (e.g., Redshift, BigQuery, Snowflake).
• Strong analytical and statistical skills; fluency in Python or R.
• Experience with machine learning libraries (e.g., scikit-learn, XGBoost) and data visualization tools (e.g., Tableau, Looker, Plotly).
• Solid understanding of experimental design, hypothesis testing, and causal inference.
• Ability to distill complex data problems into clear, actionable insights.
• BSc/MSc/PhD in a quantitative field such as Statistics, Computer Science, Mathematics, Economics, or similar.

Experience with the following would be a plus

• Experience with deep learning, NLP, or time-series forecasting.
• Knowledge of tools for building production data pipelines (e.g., Airflow, dbt).
• Familiarity with business domains like fintech, e-commerce, healthcare, etc.

What we can offer you

• Culture - We put our people first and prioritize the well-being of every team member. We have built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human.
• Learning - We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks.
• Compensation - You’ll receive an attractive salary, pension, health insurance, annual bonus, plus other benefits.
• The opportunity to drive impact through data in a high-growth environment.
• A collaborative culture with room to grow and experiment.
• Access to rich data and a modern analytics stack.

What to expect in the hiring process

• A preliminary phone call with the recruiter
• A coding exercise on HackerRank – covering core data science theory (math, statistics, linear algebra) and Python fundamentals (data structures & algorithms).
• A take-home assignment
• A technical interview with a Lead in our Engineering Team to review your take-home assignment in depth
• A behavioural and technical interview with a member of our Executive team

Moniepoint is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and candidates.

Technical Financial Crime Manager

About Paystack

Over the past nine years, Paystack has established itself as a pioneer in African fintech with a mission to help merchants get paid by anyone, anywhere in the world. Processing over $300 million in monthly transactions, our modern payments infrastructure supports tens of thousands of notable corporations, including MTN, Bolt, and Domino’s Pizza.

As we enter a phase of accelerated growth, we are seeking a Technical Financial Crime Manager to own, design, and scale our fraud and AML detection capabilities. This role sits at the intersection of data, engineering, and financial crime operations, with end-to-end accountability for ensuring our monitoring systems are technically robust, domain-accurate, and scalable across multiple markets.

This is a hands-on technical leadership role. You will define detection logic, guide system design, and directly influence how financial crime risk is identified and managed at Paystack, while also leading and developing high-performing fraud and AML teams.

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.

Why This Role Matters

This role is foundational to Paystack’s ability to scale safely. You will define how financial crime detection works at Paystack, combining strong technical systems with sound domain judgment. Success in this role directly protects customers, merchants, partners, and the broader financial ecosystem while enabling Paystack’s continued growth across Africa and beyond.

This role is open to candidates based in Nigeria, Ghana, Kenya, or South Africa