Machine Learning Data Analyst

Incode; United States; Remote; USD 70,000 - 120,000; Full time

Job summary

Incode is seeking a Machine Learning Data Analyst to enhance data quality and build automated pipelines for ML models in identity verification. The role focuses on ensuring reliable data for model performance and scalable data workflows.

You will collaborate with ML engineers and product stakeholders to automate evaluation metrics, establish data quality standards, and create dashboards that track model effectiveness across verification scenarios.

Qualifications

• Proficient in SQL and Python for data analysis and pipeline development.
• Experience with data quality frameworks and data governance is a plus.

Responsibilities

• Establish data quality standards and monitor data integrity across pipelines.
• Automate evaluation metrics for ML models and track performance.
• Build and maintain dashboards to monitor data and model performance.
• Collaborate with ML engineers and product stakeholders to prioritize data tasks.

Skills

SQL

Python

Data infrastructure

Job description

Incode is looking for a Machine Learning Data Analyst to enhance data quality and build automated pipelines for ML models in identity verification. Candidates with strong SQL, Python, and data infrastructure experience should apply.

Incode specializes in identity verification solutions, aiming to transform how individuals authenticate online. As a Machine Learning Data Analyst, you will be pivotal in investigating data quality issues and developing reliable data pipelines to support ML model performance. Your work will directly impact the integrity of ML outputs and the overall success of the company's identity solutions.

You will collaborate closely with ML engineers and product stakeholders to prioritize tasks and improve internal tooling. Your responsibilities include establishing data quality standards, automating evaluation metrics, and creating dashboards for performance tracking. Success in this role means ensuring that data pipelines are efficient and that models are evaluated accurately against real-world scenarios.