Machine Learning Specialist

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.

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.

Machine Learning Specialist

The role

Machine Learning Specialist

The role

What you will do

What you need

Strongly Preferred

Pay and benefits

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