Key Responsibilities:

Data Strategy & Infrastructure

• Together with the Director of Products and Innovation develop and execute a Data & AI Implementation Strategy aligned with organizational goals.
• Lead data transformation initiatives in data infrastructure, analytics, AI products, automation.
• Define and enforce data governance frameworks, policies, and ethical AI guidelines..
• Standardize and govern all data sources (client, portfolio, operations, external).
• Drive adoption of data-driven decision-making across departments.

Data Infrastructure & Engineering

• Oversee design and management of data warehouses, data lakes, and integration pipelines.
• Design and maintain scalable, multi-country data architecture.
• Implement scalable cloud-based data platforms (e.g., Azure, AWS, GCP).
• Improve data reliability, consistency, and accessibility across the organization.

AI/ML Product Leadership

• Lead design, training, testing, and production deployment of AI solutions (credit scoring for thin-file clients, predictive risk models, NLP for WhatsApp/IVR, impact forecasting).
• Champion responsible AI: bias mitigation, explainability, and ethical guardrails.
• Lead development of AI/ML models, NLP systems, generative AI solutions, and automation tools.
• Manage AI lifecycle: problem definition → data preparation → modeling → evaluation → deployment.
• Introduce automation (RPA, intelligent workflows) to improve operational efficiency.

Governance & Compliance

• Own data governance framework: quality, lineage, access controls, and IP protection.
• Lead enterprise-wide master data management practices.
• Ensure full compliance with GDPR, central-bank rules, donor requirements, and Inkomoko’s IP Policy.

Insights, Analytics & BI

• Deliver executive dashboards and drive data literacy across Programs, Credit, Finance, and Leadership.Lead predictive analytics projects for forecasting, segmentation, and decision intelligence.
• Introduce self-service BI tools and build analytical capacity across teams.
• Lead predictive analytics projects for forecasting, segmentation, and decision intelligence.
• Introduce self-service BI tools and build analytical capacity across teams.

Team & Culture

• Build a high-performing, interdisciplinary Data & AI team.
• Mentor staff and build data literacy across departments.
• Oversee vendor relationships and implementation partners.

Requirements

WHO WE ARE LOOKING FOR

• Master’s or Bachelor’s in Data Science, Computer Science, Statistics, or equivalent.
• 10+ years in data engineering, analytics, or ML; at least 5 years leading teams.
• Hands-on experience shipping production ML models, ideally in credit or financial inclusion.
• Expertise in modern data stacks (dbt, Snowflake/AWS, Airflow, etc.) and ML frameworks.
• Strong understanding of data governance, privacy regulations, and ethical AI practices.
• Excellent leadership, communication, and stakeholder management skills.