Top Data Analyst Jobs in Los Angeles, CA

The Lead Data Analyst (Network) drives data-driven initiatives, dissects datasets, and provides actionable insights to enhance business performance and network efficiency.

Lead Data Analyst responsible for advanced analytics, real-time dashboards, capacity modeling, and network performance insights. Collaborates with stakeholders to identify inefficiencies, run tests and pilots, and deliver actionable recommendations to improve service provider performance and network operations.

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Support Finance and Sales by reconciling and validating revenue KPIs (ACV, ARR, renewals), building reporting datasets and dashboards, collaborating with CRM and data engineering teams, and participating in month-end and quarter-end close activities. Provide ad-hoc analysis and stakeholder-facing explanations of metric derivation and data quality.

The Data Analyst will optimize marketing funnel metrics, conduct analyses, manage dashboards, and promote data-driven decision-making within the team.

Analyze consumer and ecommerce data to inform business decisions: perform descriptive and cohort analyses, build and apply statistical and predictive models, create dashboards and visualizations, and communicate actionable insights to stakeholders.

As a Data Analyst, you'll drive innovation through data insights to support decision making and contribute to business growth.

As a Data Analyst, you will drive innovation and growth by transforming data into actionable insights, helping to improve decision-making and business outcomes.

Work independently as a healthcare analytics consultant on client projects, building strong client relationships. Use statistical tools and predictive modeling to produce business reporting and BI deliverables, perform advanced statistical analysis, create data visualizations, and operate tools such as R, SAS, SPSS, Cognos, and Business Objects. Report to the Senior Consultant.

Lead client-facing analytics engagements: translate ambiguous business questions into data models, dashboards, and KPIs; write production-quality SQL and dbt models; build BI reporting; apply AI tools to accelerate analysis; drive proactive insights; set stakeholder cadence; and mentor junior analysts to ensure adoption and delivery.

Prepare analyses for Merch & Planning, write intermediate-to-complex SQL against Snowflake, manage Tableau BI (modeling, standards, automated reports), present insights to stakeholders, partner with data engineering, and build scalable analytics solutions to inform business strategy.

Plan and run experiments, acquire and manage laboratory and pre-clinical data, clean and analyze large datasets, report insights and maintain data integrity. Support data tools, documentation, and cross-functional collaboration with engineers and clinicians.

Lead analytics and reporting for value-based contracts: build and maintain performance evaluations, calculate incentive distributions, translate business requirements into technical specs, partner with Data Engineering to ensure data quality, write SQL and Python analyses, gather stakeholder feedback, and deliver ad-hoc and executive-level insights to guide strategic decisions.

Analyze digital customer journeys and site behavior to identify friction, explain marketing performance, and provide actionable insights. Maintain dashboards, support A/B and multivariate testing, collaborate with developers on tagging and tracking, and drive optimization to improve conversion and retention.

As a Senior Marketing Data Analyst, you'll analyze revenue impacts from marketing, optimize growth strategies, evaluate channels, and establish scalable reporting systems using SQL and AI tools.

Manage and ensure integrity of safety data across systems; implement data governance, ETL, and validation; analyze trends and KPIs; build dashboards and reports; support incident investigations, audits, and cross-functional stakeholders to drive risk-reduction and performance improvement.

Perform data collection, cleaning, validation, exploratory analysis, and visualization to produce dashboards and reports. Write SQL and use Python/R and visualization tools to generate insights, support cross-functional teams, maintain documentation and data quality, and assist senior analysts on modeling and forecasting.

Provide insights and recommendations to improve end-to-end purchase customer journeys across web and app for B2C and B2B. Lead A/B testing and experimentation, ensure accurate data collection and impact measurement, build self-service reporting and visualizations, query and process data, and present findings to stakeholders and senior leadership.

Audit and reconcile marketing and lead data end-to-end, maintain campaign tracking and platform integrations, troubleshoot data flow issues, manipulate large datasets to identify discrepancies, coordinate with vendors and stakeholders, and recommend process improvements to ensure accurate marketing-to-intake reporting.

The Business Intelligence Data Analyst II supports reporting, dashboards, and data-driven insights, working closely with Business Intelligence and Data Engineering teams. Responsibilities include optimizing data structures, leading data modeling, collaborating on requirements, and monitoring data feeds.

Own end-to-end analytics for the Pro product: build production data pipelines and dbt models, define north-star metrics, design dashboards and full-funnel reporting, lead A/B experimentation and causal inference, embed AI tooling, and deliver clear data-driven recommendations to product and leadership.

Own BI analytics using a modern data stack to generate insights, build dashboards and self-serve tools, partner with stakeholders, and deploy AI-driven reporting and natural-language query solutions.

Deliver analyses, dashboards, and datasets that inform GTM and Operations. Build and maintain BI dashboards, perform ad hoc analyses, enable self-serve analytics, and collaborate with data/engineering using modern data stack tools.

Support product, content, and revenue teams by building and maintaining dashboards, performing ad hoc analyses of user engagement and monetization, validating data quality, and analyzing A/B test results to translate findings into clear insights that inform product and business decisions.

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.