Jobs at alx_africa

Executes integrated marketing and recruitment campaigns for learner and enterprise audiences in Kigali. Generates and nurtures learner and corporate leads, manages outreach, events, partnerships, social media, email, and content activities, and supports prospects through enrollment or qualified commercial conversations. Maintains CRM and recruitment data, tracks conversion and pipeline performance, prepares reports, coordinates stakeholders, and recommends improvements to meet lead, enrollment, application, meeting, and enterprise pipeline targets.

The Talent Acquisition & Employer Brand Specialist will lead recruitment efforts, build talent pipelines, and enhance ALX's employer brand through effective communication and marketing strategies.

Execute and support day-to-day community operations for ALX Lagos: run programmes and events, manage hub logistics and communication channels, engage learners and alumni, capture user-generated content, track engagement metrics, support ambassador and partnership activities, and report on community performance.

Lead ALG's Impact function to build rigorous, scalable evidence systems: design and run evaluations (RCTs, quasi-experimental, longitudinal), partner with external validators, embed measurement across the learner journey, strengthen data pipelines and governance, create donor- and public-facing evidence, and lead the Impact team to turn evidence into learning, fundraising, and improved programs.

Build and maintain dashboards and reports, write and optimize SQL and Python for ETL and analysis, ensure data quality, define metrics with stakeholders, document analyses, and present insights to technical and non-technical audiences. Remote role within Africa.

Design and implement ALX's competency navigation engine: a probabilistic, cold-start path-recommendation model. Build production Python pipelines, design evaluations, encode prerequisite domain structures, and evolve models as learner data appears. Develop LLM-based processing and later behavioural and at-risk profiling models as data accumulates.

Own and productionize AI/LLM products: convert prototypes into maintained, observable services; design agent context and tooling; build service interfaces for learner artifacts; ensure long-running agents remain reliable with clear failure/recovery modes; implement eval loops and regression checks; collaborate with engineers, product managers, and data scientists.

Build, run and maintain evaluation suites for LLM products, decompose product failure modes, keep evals cost-effective, report findings to stakeholders, steward core datasets (including sensitive support data), and collaborate with AI engineers and product managers on instrumentation. Growth path from close collaboration with technical lead to full ownership of the analyzer function.