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.