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.
Our platform powers six core analytical pillars: crop health monitoring, yield prediction, risk profiling, farm boundary detection, credit risk, and market price forecasting. We achieve 80–98% accuracy through a hybrid approach that fuses multi-source satellite data, ML models, and validated ground-truth data, a combination that outperforms single-source competitors.
About the Role
Nuru is at an inflection point. We have validated product-market fit with, have a growing institutional pipeline, and proven model accuracy across multiple countries. As we scale from pilot delivery to commercial-grade operations, we need a senior ML leader who will own the integrity, reproducibility, and continuous improvement of every model we ship.
This person will be responsible for transforming Nuru’s ML function from a talented-but-informal operation into a rigorous, scalable, and auditable system that institutional clients can rely on.
What You Will Do
1.
Model Ownership & Lifecycle
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Own the complete ML lifecycle
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Lead model development, training, validation, deployment, and ongoing performance monitoring for all production models.
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Architect and maintain reproducible ML pipelines on AWS, ensuring all models are version-controlled, documented, and independently reproducible.
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Drive multi-crop expansion (from maize to beans, sorghum, potatoes, and horticultural crops) and cross-country model generalisation across diverse agroecological zones and cropping calendars.
2.
Governance, Validation & Quality
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Own and enforce Nuru’s Model Validation Protocol, including the Test 1 / Test 2 distinction: internal holdout results (Test 1) are for internal use only; independent field validation (Test 2) is the sole metric approved for external reporting.
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Execute and maintain Model Validation & Sign-Off Reports for all production models (19 models currently require individual sign-off).
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Lead Quarterly Model Governance Reviews, documenting model health, drift, and accuracy trends.
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Enforce the model change protocol: no model modification ships without documented justification, before/after accuracy comparisons, and sign-off.
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Establish pre-delivery quality assurance for all client-facing datasets and analytics, including automated checks for data integrity issues (e.g., impossible values, distribution anomalies).
3.
Ground-Truth & Data Strategy
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Design and oversee ground-truth data collection strategies, integrating field surveys (KoboToolbox), drone imagery, crop-cut samples, and in-person validation.
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Work with sparse, noisy, and incomplete ground-truth data typical of smallholder agriculture contexts, developing robust approaches to training and validation under data scarcity.
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Collaborate with operations teams across Kenya, Malawi, Nigeria, and Somalia to ensure field data quality and timeliness.
4.
Team Leadership & Stakeholder Communication
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Mentor and develop junior data science team members, establishing standards for code quality, documentation, and peer review.
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Collaborate with product, engineering, and client-facing teams to translate model capabilities into actionable intelligence delivered via dashboards, APIs, SMS/WhatsApp, and client reports.
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Defend model methodology and accuracy claims to institutional partners, including actuaries, risk analysts, and underwriters at organisations like Swiss Re and FSD Africa.
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Present technical findings clearly to non-technical stakeholders, including investors, board members, and partner executives.
What You Have
Must-Haves (Required)
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7+ years of professional experience in machine learning, with demonstrated expertise in geospatial ML, remote sensing, or agricultural applications.
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Hands-on experience with satellite imagery analysis (Sentinel, Planet Labs, or similar), vegetation indices, and time-series modelling for crop or environmental applications.
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Proven track record building ML governance and quality systems — ideally in environments where formal processes did not previously exist.
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Strong MLOps foundation: version control (Git), model registry, experiment tracking, reproducible training pipelines, and deployment automation.
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Experience managing or mentoring small technical teams (2–5 people) in fast-moving, resource-constrained environments.
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Comfort working with sparse, noisy, or incomplete datasets and designing robust validation approaches under data scarcity.
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Ability to communicate technical complexity clearly and credibly to institutional clients, investors, and non-technical leadership.
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Self-directed problem-solver who thrives in early-stage environments where you build the systems, not just use them.
Strongly Preferred
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Understanding of agricultural systems and smallholder farming contexts in East or Southern Africa.
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Experience with AWS cloud infrastructure (S3, EC2/ECS, IAM) for ML workloads.
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Familiarity with insurance, credit risk, or financial product design in agricultural or development contexts.
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Experience with ensemble methods (Prophet, LSTM, XGBoost), CNNs, and foundation models (SAM or similar) in production settings.
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Prior work with ground-truth data collection programmes (crop cuts, field surveys, drone validation).
What We Offer
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A company recognised as one of the 30 most promising African startups
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A validated impact: 25,000 farmers served
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Direct collaboration with the CEO and a lean, mission-driven team across four countries.
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The opportunity to build the ML governance and infrastructure layer for a platform that is becoming critical data infrastructure for African agrifinance.