Sobre este puesto de Machine Learning Specialist en Talent Safari
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.
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).
What We Offer
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.
