Top Data Analyst Jobs in Los Angeles, CA

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.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

The Business Intelligence / Data Analyst will optimize the data reporting pipeline using Google Big Query and Omni, work with product management, implement reports, and conduct ongoing maintenance.

Perform data extraction from SQL/NoSQL systems, validate and transform data, and run statistical and psychometric analyses (IRT, reliability, norming). Validate assessment logic, define quality metrics, design research and simulation studies, and present findings via reports and dashboards. Collaborate cross-functionally and maintain current psychometric and data engineering practices.

Partner with GTM, Clinical Strategy, and Finance to build and maintain dbt models, client-facing reports, and dashboards. Perform SQL-based analyses, ROI and clinical outcomes reporting, and translate insights for non-technical stakeholders. Ensure data reliability through testing, code review, and collaboration with Data Engineering; support ad-hoc requests and automation of reporting.

Perform full-stack on-chain and product analytics: query on-chain data, profile ecosystem actors, benchmark protocols, define KPIs, build dashboards, and translate findings into actionable briefs for strategy, BD, product, and growth teams.

As a Data Analyst, you will create dashboards, analyze data, and ensure data quality while working with the VP of Finance.

Support pharmaceutical brand and commercial teams by transforming and integrating healthcare and media data using SQL and cloud warehouses, performing EDA and statistical analysis, preparing datasets for advanced analytics and ML, and building visualizations and dashboards. Communicate insights to cross-functional stakeholders, document methodologies, and maintain analytical workflows and data governance for marketing and analytics use cases.

Analyze large healthcare datasets and develop complex SQL queries using Azure Synapse/Databricks. Build Power BI reports, produce monthly reconciliations and ad-hoc analyses in Excel/PowerPoint, support payments to value-based specialists, evaluate and expand value-based programs, automate processes with Power Automate/Power Apps, and communicate findings to senior leadership while spearheading new technologies.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.

Support data collection, cleaning, and preliminary analysis. Write and run SQL queries, use statistical tools and Python, produce visualizations, document methods, troubleshoot analytics tools, and collaborate with cross-functional teams to deliver actionable insights.

Develop and maintain payment analytics: define KPIs, build automated dashboards and reporting tables, analyze transactions and declines, investigate payment issues, run ad hoc analyses, and present actionable insights to stakeholders.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Lead delivery for Billing and Data teams, manage backlogs, conduct UAT, ensure releases meet standards, and maintain platform knowledge.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.

The Senior Data Analyst oversees performance reporting tools, automates data pipelines, conducts quantitative analyses, and communicates findings to various stakeholders.

Clean, transform, process, and query programmatic datasets to support educational programs. Ensure data integrity through testing, auditing, and documentation. Build dashboards, automate workflows, identify operational bottlenecks, and provide cross-functional analytical support. Participate in annual student application evaluations and collaborate with program, data, and engineering teams to drive data-driven operational improvements.

Own RevOps data models and BI infrastructure to drive GTM strategy. Clean and analyze datasets, build predictive models, define and track leading/lagging KPIs, deploy dashboards, document data definitions, and deliver executive-ready insights while partnering cross-functionally with Finance and GTM teams.

Own and build the support reporting layer: ticket volume, SLA, resolution times, CSAT/CES/sentiment, and capacity. Produce dashboards and analyses for leadership and engineering, maintain data quality and governance, prioritize issues using multi-signal models, and surface Voice of Customer trends to close the loop with Product and GTM.

Analyze medical claims (inpatient, outpatient, SNF, rehab, professional, DME) to identify utilization and root causes. Pull/integrate data, perform QA, present findings, maintain attribution methodology, coordinate stakeholders, and develop tools. Primary coding: Hive, SQL, Python.

Lead analytics work across revenue, product, customer, and operations by building dashboards, conducting deep-dive analyses, defining metrics and reporting standards, validating data quality with Data Engineering, and using AI tools to automate reporting and accelerate insights. Communicate findings to technical and non-technical stakeholders and proactively surface trends and opportunities.

Own and govern supply chain master data (items, BOMs, vendors, costs, units, MRP settings). Define standards, run audits, support ERP/ planning system configuration and enhancements, train users, and support cross-functional teams on data setup and lifecycle changes.

Lead product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.

Design and implement BI solutions: build data models, automations, ETL/ELT workflows, dashboards, and ad-hoc analyses. Monitor and analyze RF/video performance using Dataminer/Qligent or similar tools. Collaborate with cross-functional teams, document requirements, and deliver actionable insights to optimize hybrid terrestrial and satellite video delivery.