Top Senior Data & Analytics Jobs in Rochester, NY

Lead payments risk efforts by owning Sift and Plaid Signal, designing identity and fraud controls, reducing loss/returns, and running cross-functional risk programs with ACH Operations. Partner with Product and Engineering, perform hands-on analysis using Python and SQL against the data warehouse, and manage a small, growing analyst team.

Lead marketing measurement: own attribution, incrementality testing, and media mix modeling. Design and analyze experiments, build repeatable templates and self-serve analytics, translate results into budget and growth recommendations, ensure data integrity, and embed AI tools into analysis workflows to measure pipeline and revenue impact.

Lead advanced analytics and predictive modeling for readiness-based spares and sustainment decisions. Translate business problems into analytical frameworks, build predictive/diagnostic models, document data pipelines, mentor junior analysts, and collaborate with engineering and program stakeholders to support Navy and Department of War programs.

The Senior Data Scientist will analyze and optimize marketing funnels, build statistical models, conduct experiments, collaborate with teams, and mentor junior members.

Own and build audit-ready data architecture, ETL pipelines, and validations to support internal audit. Maintain inventories, playbooks, and catalogs; assess downstream impacts of schema changes; implement automated data quality checks and monitoring; and translate audit/business requirements into technical specifications for stakeholders.

Partner with GTM leaders to turn large datasets into actionable insight, build dashboards and forecasting models, analyze pipeline/retention/productivity, and implement automation and AI-enabled workflows to improve revenue performance and GTM operations.

Partner with cross-functional teams to design experiments, analyze large datasets, build dashboards and metrics, identify trends and customer behaviors, improve data quality and instrumentation, and provide actionable recommendations that drive strategic decisions and operational improvements.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Design and analyze experiments, measure campaign incrementality, identify customer segments and engagement opportunities, validate tracking and anomalies, surface actionable insights, and build scalable measurement infrastructure with cross-functional partners.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.