Top Senior Data & Analytics Jobs in Los Angeles, CA

Own measurement strategy and performance improvement for Snap’s advertising measurement solutions. Lead experimental design, causal analytics, benchmarking, meta-analyses, governance, and scalable measurement initiatives. Advise executives and external partners, influence product roadmaps, develop thought leadership, and build analytical fluency across teams. Apply advanced statistics, SQL, AI tools, dashboarding, and data visualization to guide advertising efficacy decisions and identify innovation opportunities.

Provides analytics and strategic advisory support for mergers, acquisitions, and other business transactions. Analyzes contract data to identify risks, obligations, performance insights, and compliance indicators; develops algorithms, data models, pipelines, dashboards, and predictive models using Python and machine learning. Collaborates with clients and internal teams, validates data integrity and security, mentors junior staff, and delivers data-driven recommendations.

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.

Leads global contact center capacity planning, forecasting, headcount, and hiring strategies. Builds predictive staffing models using Python, analyzes contact flows and staffing accuracy, and develops reports with MicroStrategy, Alteryx, and Power BI. Communicates data-driven recommendations to executives, partner managers, vendors, and operations teams while managing planning initiatives and optimizing service levels, customer experience, and costs.

Designs, develops, and maintains GCP-based security data pipelines and data products. Builds data quality and auditing frameworks, integrates security tools through APIs, optimizes BigQuery schemas and costs, and deploys automated ETL/ELT workflows using Python, SQL, and GCP services. Partners with security teams to deliver reporting views and dashboards. Cribl Stream experience is preferred for processing, enriching, routing, and reducing security telemetry and logs.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Provides data analytics consulting for enterprise clients using Dynatrace observability, digital experience monitoring, and business analytics data. Manages client relationships, performs ad hoc analysis, creates reports and dashboards, presents findings to technical and executive audiences, configures tools, trains users, and influences product improvements. The role translates complex performance and engagement data into strategies that improve digital experiences and business outcomes.