Top Remote Artificial Intelligence Data & Analytics Jobs

As a Data Platform Engineer, you'll architect and implement data services, build data pipelines, optimize for real-time risk assessment, ensure data governance, and collaborate across teams.

The Databricks Architect leads data engineering solutions, designing scalable Databricks platforms and managing cloud implementations. They work with clients, ensuring technical alignment and project execution.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

The Data Infrastructure Engineer will centralize data capabilities, improve data pipeline architecture, and ensure efficient data access across teams while optimizing GCP usage.

Own and build reliable analytics data products: define metrics, create and maintain dbt models, design dashboards for growth/finance/ops, improve data quality via testing and monitoring, and enable self-serve analytics for stakeholders.

As a Data Scientist, you will analyze data, develop models, and provide actionable insights using machine learning and statistical methods. Consult on data mining and implement data-driven solutions while maintaining knowledge in emerging analytics techniques.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

The Senior Analyst of People Analytics will manage Visier platform, support data pipelines, validate data accuracy, and develop self-service dashboards ensuring analytical integrity across employee lifecycle processes.

Lead development of large-scale data analysis and validation workflows for autonomous driving ML. Design metrics, build datasets, run statistical error and regression analyses, collaborate with ML and software teams, and maintain AWS/Kubernetes orchestration for large-scale evaluation.