Source Job

• Own the design and development of scalable, reliable data assets that drive real-time fraud detection, dispute operations, and regulatory reporting.
• Lead the creation of high-quality labeled datasets that power supervised machine learning, rules-based fraud detection and predictive chargeback modeling
• Build and maintain real-time dashboards and reporting mechanisms that provide leadership, operations and product teams with visibility into critical KPIs across Risk, Fraud and Disputes

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office.

• Develop and deploy credit scoring and behavioural models.
• Design and run experiments to optimise approval rates.
• Partner with product squads to embed decision logic into real-time systems.

Moniepoint is a global fintech building modern financial services for millions of people and businesses across high-growth markets.

• Partner with stakeholders to translate business challenges into data science problems.
• Own the end-to-end lifecycle of data science models, from data exploration to deployment.
• Develop scalable and efficient models, balancing algorithmic complexity with business needs.

Ocrolus helps lenders automate workflows with confidence, streamlining how financial institutions evaluate borrowers and enabling faster, more accurate lending decisions.

Design and validate rule-based and machine learning models for transaction monitoring. Build and optimize scalable data pipelines integrating blockchain analytics. Lead the development and execution of comprehensive metrics and data reporting frameworks.

OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps).

This role is pivotal in building and scaling a team responsible for pricing, credit limit modelling, and production credit model deployment. You’ll work closely with our Consumer Credit, Product, and Engineering teams to shape how we assess and price risk, design credit products, and measure outcomes across the credit lifecycle. You’ll build the analytics and modelling foundations that inform underwriting, customer acquisition, retention, and portfolio performance at scale.

Moniepoint is a global fintech building modern financial services for millions of people and businesses across high-growth markets.

The Senior Data Scientist will solve meaningful problems, collaborate across teams, and turn complex data into clear, actionable insights. They will 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. They will design and deliver end-to-end data science solutions that support product innovation and business strategy.

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.

• Contribute to the ML Roadmap : Identify and prioritize AI/ML opportunities across key areas such as credit risk, fraud detection, customer acquisition, and churn prevention
• Prototype & Scale : Design, build, and deploy ML models that brings value to HALA, starting with rapid prototyping and scaling up based on impact
• Data Strategy : Collaborate with engineering, product, and business teams to improve data collection practices and ensure we’re capturing the right signals for modeling

HALA is a leading fintech player in the MENAP region that aims to redefine financial services and build the future bank of SMEs. Founded in 2017, HALA has a diverse culture that encourages innovation and flexibility.

• Building, maintaining and enhancing credit risk models for lending portfolios.
• Extract, clean and manipulate large data sets using SQL and Python; build pipelines and analytics to perform model and portfolio monitoring.
• Perform exploratory data analysis (EDA) to identify portfolio trends, drivers of loss performance and provide insight into model deviations.

Achieve is a digital personal finance company that provides personalized financial solutions. They leverage data and analytics to offer personal loans, home equity loans, debt consolidation, and financial tools. They have over 3,000 employees and a strong people-first culture.

• Evaluate traditional, alternative, transactional, and raw datasets for use in underwriting, portfolio management, collections, and fraud.
• Lead quantitative due diligence for M&A targets and data partnerships, assessing data quality, depth, coverage, stability, and scalability.
• Design and implement validation frameworks to measure predictive lift, segmentation value, and incremental performance versus incumbent data.

Experian is a global data and technology company, powering opportunities for people and businesses around the world. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), they have a team of 22,500 people across 32 countries and corporate headquarters are in Dublin, Ireland.

• Design and evaluate strategies to reduce fraud and abuse across promotions, referrals, refunds, and payment flows.
• Develop and improve machine learning models, rules-based systems, and heuristics to detect high-risk behavior while minimizing false positives.
• Analyze experimental and observational data using A/B testing and causal inference to measure the effectiveness of interventions in payments and fraud prevention.

Lime is the largest global shared micromobility business. Their electric bikes and scooters have powered more than one billion rides in cities around the world.