AI role · Open offer

ML Engineer — credit & IoT analytics

M-KOPA

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Apic Centre, Nairobi, NA 00100, Kenya · Hybrid
Compensation: KES 350k–550k/month + bonus
Experience level: Senior
Team: Data science
Benefits & perks

About this role

What the team is looking for and how this position fits.

M-KOPA is hiring a Senior ML Engineer — credit & IoT analytics on the Data science team (Hybrid · Nairobi, Kenya). This is a hands-on role in robotics & industrial AI—not a generic “AI enthusiast” post.

You will own edge deployment, safety constraints, and physical systems end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

M-KOPA finances smartphones and solar devices for underbanked customers — data hub in Nairobi.

ML Engineer: credit scoring, device telemetry, and collections optimization.

Missions

Alternative credit signals from IoT and mobile money behavior.
Explainable models for regulatory and customer trust.

Stack & environment

Python, PyTorch, SQL, Airflow.

What we look for

Fintech/credit ML; hybrid Nairobi.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
AirflowTool
Scheduled ML pipelines with SLAs, backfills, and data-quality checks between stages.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Credit scoring, device telemetry ML, or collections optimization.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, Machine Learning, and Docker.
You have owned edge deployment, safety constraints, and physical systems in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Nairobi, Kenya)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

Apply

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What the employer asked for

Credit scoring, device telemetry ML, or collections optimization.

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