About the Company

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

Model Ownership & Lifecycle

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 (from maize to beans, sorghum, potatoes, and horticultural crops) and cross-country model generalisation across diverse agroecological zones and cropping calendars.

Governance, Validation & Quality

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.

Execute and maintain Model Validation & Sign-Off Reports for all production models (19 models currently require individual sign-off).

Lead Quarterly Model Governance Reviews, documenting model health, drift, and accuracy trends.

Enforce the model change protocol: no model modification ships without documented justification, before/after accuracy comparisons, and sign-off.

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).

Ground-Truth & Data Strategy

Design and oversee ground-truth data collection strategies, integrating field surveys (KoboToolbox), drone imagery, crop-cut samples, and in-person validation.

Work with sparse, noisy, and incomplete ground-truth data typical of smallholder agriculture contexts, developing robust approaches to training and validation under data scarcity.

Collaborate with operations teams across Kenya, Malawi, Nigeria, and Somalia to ensure field data quality and timeliness.

Team Leadership & Stakeholder Communication

Mentor and develop junior data science team members, establishing standards for code quality, documentation, and peer review.

Collaborate with product, engineering, and client-facing teams to translate model capabilities into actionable intelligence delivered via dashboards, APIs, SMS/WhatsApp, and client reports.

Defend model methodology and accuracy claims to institutional partners, including actuaries, risk analysts, and underwriters at organisations like Swiss Re and FSD Africa.

Present technical findings clearly to non-technical stakeholders, including investors, board members, and partner executives.

What You Have

Must-Haves (Required)

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.