Machine Learning Specialist

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.