Top Remote Artificial Intelligence Data & Analytics Jobs

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.

The role involves enhancing the client's BI platform by managing data governance projects, documenting strategies, policies, and standards for effective data management.

The Senior Value Attainment Analyst will analyze customer data to create ROI analyses that support renewals and expansions, ensuring accurate methodologies and compelling presentations for health system leadership.

The Director of Data Analytics & Business Intelligence leads analytics functions, building models and dashboards while guiding a team. Responsibilities include analytics infrastructure management, cross-functional collaboration, and driving AI/ML initiatives.

Hands-on Senior Data Engineer to maintain and optimize an Oracle-centric enterprise data warehouse, manage cloud-to-Oracle integrations, evolve ETL/ELT pipelines, and help build a hybrid lakehouse (Apache Iceberg, OCI Object Storage, Parquet). Requires performance tuning, Linux administration, Java debugging, and close collaboration with senior client stakeholders. Contractor role with possible conversion to full-time.

Perform part-time quality audits and evaluations of task outputs, define and refine quality benchmarks and scoring rubrics, analyze audit results to produce actionable insights and reports, and recommend process improvements to raise output quality.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.