Senior Data Scientist - Credit/Underwriting

Who we are: 

Fido empowers millions across Africa to take control of their finances with ease. As a leader in cutting-edge financial technology,

Fido clears the way for building credit, securing instant loans, making smart investments, and obtaining tailored insurance. No banker’s hours, no hidden fees – just endless opportunities.

From city centers to rural communities, Fido is breaking barriers and creating financial freedom, providing access to innovative tools and services that foster growth and empowerment. By leveraging advanced technology, 

Fido is shaping a future of opportunity and financial inclusion across the continent.

Join the team and be a part of leading this transformative change, driving impact where it matters most.

What you will do: 

At Fido, you’ll shape the next generation of credit scoring and fraud detection models that empower millions across Africa. Working closely with our CRO, data science, and data ops teams, you’ll bring advanced models into production pipelines that are robust, scalable, and impactful. You’ll continuously monitor and recalibrate performance through A/B testing and ongoing evaluation, while translating complex insights into clear, actionable recommendations for stakeholders.

You’ll lead feature ideation and research with a focus on financial impact, accelerate deployment with our data engineering and MLOps teams, and collaborate with risk managers to turn observed patterns into smarter models. Beyond model building, you’ll design profitability-driven frameworks, inventorying rapid experimentation pipelines, and serve as Fido’s credit and fraud domain expert— others and guiding the CRO on monitoring, recalibration, and usage decisions.

Who you are:

• 5+ years’ experience in credit risk analytics in emerging markets (e.g., digital lenders, data-driven financial platforms, payment service providers, etc.)
  
• 5+ years’ experience in fraud modelling/transactional risk at fintech or e-commerce scale-ups
  

• Strong applied data science skills (Python, SQL, scikit-learn, ML pipelines)
  
• Experience in credit/fraud feature engineering, from alternative data to transactional behaviors
  
• Solid understanding of loan economics: delinquency curves, loss given default, provisioning, portfolio segmentation
  
• Proven ability to balance statistical performance with business constraints (risk appetite, growth targets, regulatory caps)
  
• Excellent communication and stakeholder management — Able to bridge CRO, finance, collections, and data engineering