JOB DESCRIPTION
Machine Learning Specialist Nairobi, Kenya (Remote Considered)
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
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 YOU’LL JOIN
- 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
Pay: $40.00-$50.00 per hour
Expected hours: 40.0 per week
Work Location: Remote
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