Job Opportunities API

Company Kora is the marketplace for everything payments. We offer a robust payment API for payment collections, disbursements, and conversions for businesses anywhere in Africa.  Our vision, which is at the core of what we do every day, is to create a world void of digital financial barriers. We are committed to delivering reliable, secure, and easy-to-use digital financial solutions to every single customer with a guarantee that it is improving their lives. To achieve this mission, we need people like you.  We strongly believe in our ability to find Water in the Desert and pick the Sands in the Ocean. We value positive energy and clear communication, and are committed to building an inclusive environment for people from every background. Role Summary We run payments across Africa and are now positioned as a global fiat and stablecoin payment infrastructure. We offer mobile money, virtual bank accounts, and virtual cards for payins and payouts across multiple markets. Our data infrastructure is batch-first (Airflow + a cloud data warehouse) and we use Vertex AI for our MLOps lifecycle. The ML team is high-ownership: you will build models, design systems, ship them, and observe them in production. You will work on merchant-facing intelligence: forecasting, anomaly detection, segmentation, as well as automation and product-layer ML. If you want to build practical things that matter in a context that most ML engineers never get near, this is the role. What You'll Work On Design and ship a per-merchant payment volume forecasting system: time-series decomposition, Africa-specific event calendars (salary cycles, MNO maintenance windows, public holidays), quantile regression for uncertainty bounds Build and maintain fraud/ anomaly detection across the payment stack (residual-based and model-driven) with tiered alerting logic mapped to merchant risk profiles. Own the dynamic merchant segmentation system end-to-end: rule-based and data-driven hybrid, percentile thresholds grounded in EDA, segment-transition features as ML inputs Instrument and monitor deployed models: drift detection, retraining triggers, and evaluation pipelines via Vertex AI Build automation tooling that sits alongside the core ML work: Airflow DAGs, pipeline scaffolding, and tooling to reduce operational toil Contribute to product and strategic thinking. Requirements Our Stack Apache Spark and Airflow Google Vertex AI Python SQL GCS/BigQuery What We're Looking For 3+ years as an ML engineer in a production environment Strong Python and comfort with Spark for large-scale data processing Experience with time-series modelling: decomposition, forecasting, anomaly detection Solid grasp of the ML lifecycle as a unified discipline Ability to work with batch infrastructure and design for it deliberately High ownership mentality: you notice problems and fix them as opposed to waiting to be assigned Ability to identify gaps in data-driven business processes and come up with solutions Strong plus: Familiarity with Vertex AI (custom training jobs, model registry, pipelines, monitoring) Experience in payments, fintech, or any domain where label quality, distribution shift, and operational constraints are real problems Exposure to African market dynamics n8n or similar automation/workflow tooling experience Important You will be evaluated less on credentials or certifications and more on the quality of your thinking. In this team, a strong ML engineer: Can explain why a design decision was made and what it trades off Writes systems that the next person can understand and build on Is honest about model limitations, especially in production contexts where overconfidence causes real loss Closes the loop between model outputs and business outcomes without needing to be told to Benefits Health insurance Sponsored and tailored training Paid parental leave Paid time-off Flexible work style Low-intere