Top Data & Analytics Jobs

The Quantitative Methodologist will design statistically rigorous studies, apply advanced analytics, interpret results, ensure quality, and communicate insights to clients.

Work with clients to build analytics and AI solutions, create data products and dashboards, apply statistical models, validate data with SQL, design analyses in R/Tableau/SAS/Visual Basic/Excel, guide teams, and communicate insights to drive business outcomes.

Client-facing solutions architect who designs and delivers scalable cloud, data, and AI-enabled solutions. Leads end-to-end projects, mentors teams, ensures scalable/resilient architectures, modernizes data ecosystems, drives delivery methodology, and develops business cases while collaborating globally with clients and ZS teams.

Designs and maintains governed, analytically ready datasets for AML/KYC operations. Builds scalable ETL and transformation processes using SQL and Python, develops metadata and semantic layers for AI and self-service analytics, and implements data quality, reconciliation, and validation frameworks. Partners with operations, data, technology, and AI teams while supporting production data assets, documenting findings, addressing source-system impacts, and ensuring compliance with controls, privacy, and regulatory standards.

Leads the design, development, and operation of production-grade data pipelines and governed data products supporting operational resiliency and recovery-risk analytics. Responsibilities include data modeling, ETL, database optimization, data quality and lineage, cloud and event-driven architectures, automation, testing, and CI/CD. Partners with cybersecurity, technology controls, engineers, architects, analysts, and business stakeholders to translate risk and control requirements into scalable, auditable solutions.

Analyzes large, complex datasets from multiple sources to support Inspector General investigations. Responsibilities include data extraction, quality validation, discrepancy resolution, statistical and narrative reporting, dashboard and BI solution development, SQL querying, application and script modification, process documentation, and presenting findings. The role requires substantial data analysis and management experience, programming knowledge, and familiarity with visualization, database, statistical, and reporting tools.

Conduct quantitative and qualitative analysis supporting global go-to-market and field services teams. Analyze retention, churn, product adoption, forecasting, territory design, and performance data; build dashboards, reports, queries, and automated data pipelines using Python and SQL. Partner with systems, operations, sales, and executive stakeholders to ensure data quality, prioritize requests, operationalize insights, and drive revenue-impacting decisions.

Leads enterprise competitive intelligence capabilities, including data collection, matching, scraping, governance, quality standards, analytical methodologies, and AI-enabled workflows. Partners with Product, Engineering, Data Science, Merchandising, Pricing, Digital, Marketplace, and Strategy teams to translate business needs into scalable solutions. Develops metrics, models, validation processes, and insights that support business decisions, while potentially managing and developing a small analytics team.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

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.

Designs and maintains scalable data pipelines, models, integrations, and ETL/ELT processes using cloud data technologies. Analyzes complex data, implements quality controls and automated testing, troubleshoots issues, documents data flows, and improves engineering processes. Collaborates with product owners, business stakeholders, data scientists, engineers, and architects in an agile environment while translating business needs into reliable technical data solutions.

Lead design and implementation of large-scale Azure cloud data systems and ETL/ELT pipelines using Databricks, Spark, Snowflake and LLM integrations. Build AI-driven solutions, optimize SQL performance, define architecture and best practices, mentor engineers, collaborate with stakeholders, and drive scalable, secure data platforms for analytics and machine learning.

Lead analytics engineering efforts to design and build dbt models, maintain a semantic/metrics layer, ensure Snowflake performance, establish modeling/testing/documentation standards, partner with Data Science/Product/Engineering, and deliver native warehouse data products to customers.