Top Data & Analytics Jobs in Los Angeles, CA

Investigate Terms of Service and Community Guidelines violations, identify abuse trends, and develop automated detection and enforcement rules. Analyze large datasets using SQL, create dashboards and reports, measure mitigation effectiveness, and recommend policy, process, and product improvements. Collaborate with Product, Engineering, and scaled review teams, apply LLM-based approaches, provide training, and improve internal investigation tools and safety operations.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Process and validate aircraft monitoring data, decode avionics bus data, troubleshoot C-17 systems, perform root cause analysis, develop data-mining algorithms, and provide technical recommendations to customers. The role supports program reviews, integrated product teams, design reviews, and aircraft health management for the C-17 fleet.

The Business Analyst translates complex data into actionable business insights. Responsibilities include building Looker dashboards, producing recurring and ad hoc reports, analyzing trends and performance drivers, partnering with Marketing, Sales, and Operations, and supporting process improvements. The role requires strong SQL, Excel, communication, business judgment, and independent project management skills, with preferred experience in BigQuery and insurance industry metrics.

Maintain global ERP master data, item and branch records, BOMs, planning parameters, MRP data, and supply chain documentation. Analyze data quality issues, support reporting and dashboards, ensure compliance with SOX and GMP requirements, and collaborate with Manufacturing, Quality, Regulatory, Finance, and planning teams. The role also contributes to process improvements, ERP enhancements, and transformation initiatives.

Analyze, clean, validate, and reconcile data to produce dashboards, reports, metrics, SQL queries, analytical datasets, and business insights. Partner with stakeholders to define requirements, investigate trends and anomalies, validate data models and workflows, improve data quality, and establish scalable analytics standards and documentation.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Architects and evolves enterprise-scale batch, real-time, and event-driven data platforms and pipelines. Leads data modeling, distributed processing, data quality, governance, observability, CI/CD, security, monitoring, incident response, and cost optimization. Provides technical direction across hybrid cloud environments, mentors engineers, leads critical initiatives and code reviews, manages technical debt, evaluates new technologies, and partners with business and technical stakeholders. Participates in on-call rotations and supports distributed and offshore engineering teams.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.

The Data Scientist in Finance will analyze financial data, create dashboards, collaborate on data engineering, and find improvements for the business.