Talent Safari - Machine Learning Specialist

Nairobi, Kenya, Hybrid2w ago

In Office Senior EMEA Insurance Cloud Computing Machine Learning Engineer Governance Pipeline Management Training Development Git MLOps

Requirements

• 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 • 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).

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.

Benefits

• 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.