المسمى الوظيفي

About Nuru Solutions

Nuru Solutions operates as a B2B agricultural data intelligence platform serving regions including Kenya, Malawi, Nigeria, and Somalia. It integrates satellite imagery, weather data, on-ground validation, and machine learning to provide farm-specific insights to insurers, lenders, and agribusinesses supporting smallholder farmers. The platform delivers six essential analytical functions: monitoring crop health, predicting yields, profiling risk, detecting farm boundaries, assessing credit risk, and forecasting market prices. The approach combines multi-source satellite data and validated ground-truth information to achieve 80–98% accuracy, surpassing competitors relying on single data sources.

Role Purpose

With proven product-market fit and expanding institutional demand, Nuru is transitioning from pilot operations to a scalable commercial model. This role seeks a senior machine learning leader to ensure the quality, reproducibility, and progressive enhancement of deployed models, transforming the existing ML capabilities into a robust and auditable infrastructure for institutional clients.

Key Responsibilities

Oversee the full lifecycle of ML models including development, training, validation, deployment, and performance tracking.
Design and sustain reproducible ML pipelines on AWS with strict version control, documentation, and reproducibility.
Drive expansion from maize to additional crops such as beans, sorghum, potatoes, and horticultural products across diverse agroecological zones and countries.
Implement and uphold the Model Validation Protocol, distinguishing between internal validation metrics and independent external field validations used for reporting.
Prepare and maintain detailed Model Validation & Sign-Off Reports for all production models, currently totaling 19 requiring individual approval.
Lead quarterly governance reviews documenting model stability, drift, and accuracy trends.
Enforce strict protocols for model updates including documented justification and accuracy comparisons before deployment.
Establish automated quality assurance measures for all client-facing data and analytics to detect anomalies and data integrity issues.
Design and supervise multifaceted ground-truth data collection efforts using field surveys, drone imagery, crop-cut sampling, and validation in collaboration with regional teams.
Develop robust techniques accommodating noisy, sparse, and incomplete datasets characteristic of smallholder agriculture.
Mentor junior data scientists, promote best practices in code quality, documentation, and review processes.
Coordinate with product, engineering, and client teams to translate ML models into actionable insights delivered via multiple channels including dashboards, APIs, SMS, and reports.
Confidently represent model methodologies and accuracy to institutional partners including actuaries and underwriters at organizations like Swiss Re and FSD Africa.
Effectively communicate complex technical information to non-technical audiences such as investors, board members, and partners.

Qualifications and Experience

At least 7 years in machine learning roles, with expertise in geospatial ML, remote sensing, or agricultural domains.
Practical experience analyzing satellite imagery (e.g., Sentinel, Planet Labs), vegetation indices, and crop/environmental time-series modeling.
Proven ability to establish ML governance and quality controls in organizations lacking formal model management processes.
Strong foundation in MLOps practices including Git version control, model registry, experiment tracking, reproducible pipelines, and automated deployment.
Experience managing or mentoring small technical teams in dynamic and resource-limited settings.
Proficiency in handling sparse and noisy datasets with sophisticated validation strategies adapted to data scarcity.
Effective communicator capable of clearly articulating technical complexity to institutional clients, investors, and leadership.
Highly self-motivated with a problem-solving mindset oriented towards building foundational systems in early-stage environments.

Preferred Skills

Familiarity with agricultural systems and smallholder farming in East or Southern Africa.
Experience working with AWS cloud services such as S3, EC2/ECS, and IAM for ML workloads.
Knowledge of insurance, credit risk, or financial product mechanisms in agriculture or development.
Experience applying ensemble models like Prophet, LSTM, XGBoost; CNNs; and foundational models such as SAM in production contexts.
Background in managing ground-truth data collection programs including crop cuts, field surveys, and drone validation.

Additional Information

Nuru Solutions is recognized among Africa's top 30 promising startups.
The platform currently supports over 25,000 farmers.
Opportunity to work closely with leadership and a mission-driven, multinational team.
Chance to build the ML governance framework essential for data infrastructure in African agrifinance.

Application Process

Interested candidates should submit their CV along with a summary of relevant experience. Including links to published work, open-source contributions, or project portfolios related to geospatial ML or agricultural applications is encouraged.

Machine Learning Specialist