Machine Learning Specialist

Transform Nuru's ML function into a rigorous, scalable, and auditable system. Own model development, satellite telemetry fusion, MLOps governance, and continuous accuracy improvement across production models.

Nuru is at an inflection point. We have validated product-market fit, 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.

Core Responsibilities

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 We're Looking For

Must-haves

• 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.

Strongly Preferred

• 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).