Top Data & Analytics Jobs in Richmond, VA

Leads marketing and sales analytics strategy, architecture, and measurement. Builds data models, pipelines, machine learning datasets, attribution and incrementality models, and revenue-performance analyses. Advises leaders on investments, tools, account prioritization, and funnel economics. Establishes data, metric, testing, and privacy standards; partners with Marketing, Sales, Finance, Revenue Operations, Data Engineering, and IT; plans roadmaps; presents recommendations to executives; and mentors analysts and engineers.

Own advanced advertising measurement strategy for Connected TV, including incrementality testing, lift studies, attribution, experimental design, and performance validation. Advise customers and internal teams on interpreting complex measurement results, lead analytical enablement, partner with engineering and measurement vendors, resolve ambiguous post-launch challenges, and translate cross-customer insights into product strategy, go-to-market materials, and scalable best practices.

Designs and delivers backend systems, APIs, data models, ETL workflows, data interactions, and user interfaces. Optimizes data operations for scalability, performance, integrity, and security while contributing to automated testing and CI/CD. Requires strong Python and Apache Spark experience, distributed computing knowledge, Agile experience, and data analysis skills. The role supports healthcare-focused digital services in a remote-first environment.

Analyzes fraud, credit, portfolio, alert, case, and customer data to identify trends, typologies, losses, control gaps, and process inefficiencies. Develops fraud KPIs and KRIs, supports detection models and rules, evaluates alert and investigation lifecycles, and delivers executive-ready reporting aligned with BSA/AML, regulatory, and audit standards. Partners with Fraud Operations, Compliance, Risk, Product, and Technology teams to improve detection, escalation, investigation, and resolution outcomes.

Research unclaimed-property records, clean and deduplicate datasets, conduct AI-assisted OSINT and social-media investigations, generate leads, prepare outreach files, coordinate with data partners, and report on processing volume, match rates, and lead generation.

Leads the design and development of enterprise-wide data and analytics systems using Snowflake, Python, advanced SQL, and AWS services. Requires strong data engineering expertise and experience supporting financial services organizations, with knowledge of asset management and financial markets.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Leads Gartner research and thought leadership on data management for AI enablement. Provides expert guidance on data platforms, governance, context-layer architectures, AI integrations, and secure enterprise infrastructure. Develops market insights, advises executive clients, delivers presentations, supports sales, reviews content, mentors analysts, and improves research processes. Requires strong research, writing, analytical, presentation, communication, business, and technical skills, with up to 25% travel.

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.

Designs and owns Databricks lakehouse architecture, scalable batch and streaming pipelines, data governance, analytics models, and cloud infrastructure. Leads major platform migrations, establishes engineering and CI/CD standards, manages Terraform and FinOps practices, and prototypes AI use cases. The role advises stakeholders, mentors engineers, and ensures secure, compliant, cost-effective data platforms across cloud environments.