Machine Learning Specialist

Nuru Solutions is a B2B agricultural data intelligence platform operating across Kenya, Malawi, Nigeria, and Somalia. We combine satellite imagery, weather data, ground truth, and machine learning to deliver farm-level intelligence to insurers, lenders, and agribusinesses serving smallholder farmers. Nuru is at an inflection point. We need a senior ML leader who will own the integrity, reproducibility, and continuous improvement of every model we ship.

Responsibilities

Own the complete ML lifecycle; Lead model development, training, validation, deployment, and ongoing performance monitoring for all production models; Architect and maintain reproducible ML pipelines on AWS, ensuring all models are version-controlled, documented, and independently reproducible; Drive multi-crop expansion and cross-country model generalisation; Own and enforce Nuru’s Model Validation Protocol; Execute and maintain Model Validation & Sign-Off Reports for all production models; Lead Quarterly Model Governance Reviews; Enforce the model change protocol; Establish pre-delivery quality assurance for all client-facing datasets and analytics; Design and oversee ground-truth data collection strategies; Work with sparse, noisy, and incomplete ground-truth data; Collaborate with operations teams across Kenya, Malawi, Nigeria, and Somalia; Mentor and develop junior data science team members; Collaborate with product, engineering, and client-facing teams; Defend model methodology and accuracy claims to institutional partners; Present technical findings clearly to non-technical stakeholders

Requirements

7+ years of professional experience in machine learning, with demonstrated expertise in geospatial ML, remote sensing, or agricultural applications; Hands-on experience with satellite imagery analysis (Sentinel, Planet Labs, or similar), vegetation indices, and time-series modelling for crop or environmental applications; Proven track record building ML governance and quality systems — ideally in environments where formal processes did not previously exist; Strong MLOps foundation: version control (Git), model registry, experiment tracking, reproducible training pipelines, and deployment automation; Experience managing or mentoring small technical teams (2–5 people) in fast-moving, resource-constrained environments; Comfort working with sparse, noisy, or incomplete datasets and designing robust validation approaches under data scarcity; Ability to communicate technical complexity clearly and credibly to institutional clients, investors, and non-technical leadership; Self-directed problem-solver who thrives in early-stage environments where you build the systems, not just use them

Nice to have

Understanding of agricultural systems and smallholder farming contexts in East or Southern Africa; Experience with AWS cloud infrastructure (S3, EC2/ECS, IAM) for ML workloads; Familiarity with insurance, credit risk, or financial product design in agricultural or development contexts; Experience with ensemble methods (Prophet, LSTM, XGBoost), CNNs, and foundation models (SAM or similar) in production settings; Prior work with ground-truth data collection programmes (crop cuts, field surveys, drone validation)

Benefits

A company recognised as one of the 30 most promising African startups; A validated impact: 25,000 farmers served; Direct collaboration with the CEO and a lean, mission-driven team across four countries; The opportunity to build the ML governance and infrastructure layer for a platform that is becoming critical data infrastructure for African agrifinance