{"id":158094,"url":"https://alion.io/job/talent-safari-machine-learning-specialist","title":"Machine Learning Specialist","company":{"id":450348,"name":"Talent Safari","domain":"talentsafari.io","url":"https://alion.io/company/talent-safari","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Nairobi, Kenya"],"countries":["KE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18000,"max_usd":47000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EC2","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Time Series","optional":false},{"name":"WhatsApp","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-08-26T12:57:27Z","employer_posted_date":"2026-08-26","last_verified_at":"2026-11-06T03:41:09Z","board_verified":true,"closed_at":null,"days_open":71,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":71},"description":"About the Company\nNuru 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.\nOur 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.\nAbout the Role\nNuru 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.\nThis 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.\nWhat You Will Do\nModel Ownership & Lifecycle\nOwn the complete ML lifecycle\n\nLead model development, training, validation, deployment, and ongoing performance monitoring for all production models.\n\nArchitect and maintain reproducible ML pipelines on AWS, ensuring all models are version-controlled, documented, and independently reproducible.\n\nDrive multi-crop expansion (from maize to beans, sorghum, potatoes, and horticultural crops) and cross-country model generalisation across diverse agroecological zones and cropping calendars.\n\nGovernance, Validation & Quality\nOwn 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.\n\nExecute and maintain Model Validation & Sign-Off Reports for all production models (19 models currently require individual sign-off).\n\nLead Quarterly Model Governance Reviews, documenting model health, drift, and accuracy trends.\n\nEnforce the model change protocol: no model modification ships without documented justification, before/after accuracy comparisons, and sign-off.\n\nEstablish pre-delivery quality assurance for all client-facing datasets and analytics, including automated checks for data integrity issues (e.g., impossible values, distribution anomalies).\n\nGround-Truth & Data Strategy\nDesign and oversee ground-truth data collection strategies, integrating field surveys (KoboToolbox), drone imagery, crop-cut samples, and in-person validation.\n\nWork with sparse, noisy, and incomplete ground-truth data typical of smallholder agriculture contexts, developing robust approaches to training and validation under data scarcity.\n\nCollaborate with operations teams across Kenya, Malawi, Nigeria, and Somalia to ensure field data quality and timeliness.\n\nTeam Leadership & Stakeholder Communication\nMentor and develop junior data science team members, establishing standards for code quality, documentation, and peer review.\n\nCollaborate with product, engineering, and client-facing teams to translate model capabilities into actionable intelligence delivered via dashboards, APIs, SMS/WhatsApp, and client reports.\n\nDefend model methodology and accuracy claims to institutional partners, including actuaries, risk analysts, and underwriters at organisations like Swiss Re and FSD Africa.\n\nPresent technical findings clearly to non-technical stakeholders, including investors, board members, and partner executives.\n\nWhat You Have\nMust-Haves (Required)\n7+ years of professional experience in machine learning, with demonstrated expertise in geospatial ML, remote sensing, or agricultural applications.\n\nHands-on experience with satellite imagery analysis (Sentinel, Planet Labs, or similar), vegetation indices, and time-series modelling for crop or environmental applications.\n\nProven track record building ML governance and quality systems - ideally in environments where formal processes did not previously exist.\n\nStrong MLOps foundation: version control (Git), model registry, experiment tracking, reproducible training pipelines, and deployment automation.\n\nExperience managing or mentoring small technical teams (2-5 people) in fast-moving, resource-constrained environments.\n\nComfort working with sparse, noisy, or incomplete datasets and designing robust validation approaches under data scarcity.\n\nAbility to communicate technical complexity clearly and credibly to institutional clients, investors, and non-technical leadership.\n\nSelf-directed problem-solver who thrives in early-stage environments where you build the systems, not just use them.\n\nStrongly Preferred\nUnderstanding of agricultural systems and smallholder farming contexts in East or Southern Africa.\n\nExperience with AWS cloud infrastructure (S3, EC2/ECS, IAM) for ML workloads.\n\nFamiliarity with insurance, credit risk, or financial product design in agricultural or development contexts.\n\nExperience with ensemble methods (Prophet, LSTM, XGBoost), CNNs, and foundation models (SAM or similar) in production settings.\n\nPrior work with ground-truth data collection programmes (crop cuts, field surveys, drone validation).\n\nWhat We Offer\nA company recognised as one of the 30 most promising African startups\n\nA validated impact: 25,000 farmers served\n\nDirect collaboration with the CEO and a lean, mission-driven team across four countries.\n\nThe opportunity to build the ML governance and infrastructure layer for a platform that 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