Top Data & Analytics Jobs in Los Angeles, CA

Develops dashboards, reports, KPIs, data models, and API-driven pipelines using Tableau, Power BI, SQL, Smartsheet, and related tools. Collects and validates data, analyzes trends, supports PMO reporting and maturity assessments, improves reporting automation and data quality, and provides insights for program reviews and leadership decisions. The role also supports project tracking and runs Change Board Meetings.

Build Vast’s People Analytics function from the ground up, including data governance, a unified People data model, trusted metrics, executive Tableau dashboards, and workforce analytics. Develop attrition, retention, performance, compensation, pay equity, and headcount models; evaluate People programs; and provide actionable recommendations to senior leaders. The role also establishes self-service reporting, privacy standards, analytics processes, and the roadmap for scaling the function and team.

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

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.

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.

Principal Data Analyst responsible for driving data-informed business strategy, leading end-to-end analysis, and building analytics foundations for a new product. The role includes designing data capture and pipelines, creating reporting-ready datasets, supporting governance and privacy, conducting statistical analysis, segmenting clients, partnering with product and engineering, communicating insights to executives, and mentoring analysts. The analyst will also use AI to improve effectiveness and establish reliable data sources.

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.