Posted May 25, 2026
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. ---
3+ years of experience as a Data Analyst or in a similar data infrastructure role. - Strong SQL and Python skills for data investigation and root cause analysis. - Hands-on experience with AWS Redshift or a similar columnar/cloud database (BigQuery, Snowflake, etc.). - Solid statistical foundation — you can reason about rates, distributions, significance, and sampling bias. - Hands-on experience with workflow orchestration tools (Airflow, Prefect, Dagster, etc.). - Proven experience in data quality management, data preparation, or ML data pipelines. - A proactive mindset toward identifying problems. - Strong collaboration and problem-solving skills. - Background in mathematics, physics, or engineering. Preferred Experience
Familiarity with big data technologies or data infrastructure optimization. - Experience with labeling workflows or ML data preparation pipelines. - Exposure to AWS or other cloud-based data solutions. - Interest in machine learning operations (MLOps) and scalable ML systems. ---
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