Machine Learning Data Analyst

POWER A WORLD OF TRUST

Incode is the leading provider of world-class identity solutions that is reinventing the way humans authenticate and verify their identities online to power a world of digital trust. Through our revolutionary identity solutions, we are unleashing the business potential of universal industries including finance, government, retail, hospitality, gaming, and more, by reducing fraud and transforming human interactions with data, products, and services. We’re in the process of rapidly scaling our diverse global team and we’re looking for entrepreneurial individuals and leaders who are curious, driven, and excited by ownership to join a Unicorn-status scale-up! ### The Impact You'll Make

As a Data Analyst on the ID Document Intelligence team, you'll investigate data quality issues, perform root cause analysis, and build the pipelines and automated checks that keep Incode's machine learning ecosystem reliable. You'll ensure that data flows efficiently and accurately through every stage of model training, labeling, and performance tracking. Your work will be essential to maintaining the scalability, quality, and precision of Incode's document intelligence systems used by millions worldwide. ---

What You'll Own & Drive

• Root Cause Investigation — Independently investigate data quality issues end-to-end. When a model metric drops or data looks wrong, you own the investigation: forming hypotheses, querying across data sources, and delivering a clear, evidence-backed answer. - — Design, build, and maintain pipelines for collection, labeling, validation, and metric computation that support ML training and evaluation. - — Establish and monitor consistency checks, accuracy audits, and root-cause analysis when issues impact model outcomes. - — Define, implement, and automate evaluation metrics and reporting that reflect real-world product use cases and business goals. - — Build scalable dashboards and monitoring to enable fast, data-driven decisions across teams. - — Develop and operate reliable orchestration (Airflow, Prefect, or similar) to schedule, observe, and troubleshoot end-to-end pipelines. - — Write SQL and Python to efficiently investigate problems — querying databases, calling internal APIs, and processing data across multiple sources. - — Partner closely with ML engineers, analysts, and product stakeholders to prioritize work by impact, unblock execution, and continuously improve internal tooling for analysis and evaluation. ---