Top Remote Analytics Jobs in Washington, DC

Develop and lead healthcare analytics and BI solutions for HIS products: design, code, and deliver enterprise-scale applications and dashboards, work with AWS data services and BI tools, support product development and troubleshoot reporting issues for global customers.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Lead process mapping, analysis, and redesign to improve efficiency and outcomes. Partner with stakeholders to gather requirements, analyze data (SQL, Excel, Power BI), define KPIs, apply Lean/Six Sigma methods, and support automation and digital transformation initiatives to drive growth, profitability, and loss reduction.

Lead development and governance of the supply chain data warehouse and analytics platforms. Build SQL-driven models, dashboards, and KPIs to drive inventory optimization, cost reduction, supplier performance, and working capital improvements. Support ERP integrations and data migrations, perform deep data discovery and predictive analytics, and present actionable insights to senior leaders to enable strategic decision-making.

Lead global partner analytics and planning for partner GTM motions. Build dashboards, business models, AI-enabled workflows, and executive reports to support partner planning, incentives, coverage, performance management, and cross-functional decision-making across hyperscalers, ISVs, marketplaces, and reseller channels.

Own the end-to-end analytics function embedded in a business unit (growth, product, AI/automation, or partnerships). Define problems and metrics, build reports and experiments, surface insights, recommend actions, and drive outcomes with cross-functional partners.

Builds and maintains analytical data foundations for lending, underwriting, pricing, originations, collections, and portfolio analytics. Develops reusable SQL assets, dimensional models, data marts, transformation workflows, reporting solutions, and business intelligence products using SQL, Python, dbt, Git, Snowflake, Power BI, and Sigma. Partners with technical teams, analysts, and business stakeholders to integrate data, standardize metrics, automate testing, and deliver reliable, documented, scalable analytical assets.

Embed with a core business area to define problems, build reports and experiments, analyze data, surface insights, and drive high-impact recommendations end-to-end across growth, product, AI, or partnerships.

Lead analytics for quality and HEDIS performance improvement: design and build reporting, track HEDIS rates and interventions, conduct statistical analysis and predictive modeling, manage data warehouse changes, support audits and special projects, and train stakeholders on analytic solutions.

Lead supply-and-demand forecasting for the psychiatry service line: build and maintain S&D models, integrate models into core data infrastructure, define operational metrics (capacity, clinician utilization, hiring targets), run forecast vs. actual audits, and partner cross-functionally to guide hiring, resource allocation, and operational improvements.

Lead a team to analyze sales data, build and maintain Power BI and Excel reporting, leverage AI and automation for pipeline and lead scoring, align cross-functional GTM strategies, ensure data quality, and deliver actionable, executive-ready insights to drive sales performance and quota attainment.

Lead analytics strategy and portfolio measurement for payor-focused products. Define standards for actuarial value, client reporting, and internal measurement. Align cross-functional teams (actuaries, product, reporting, analytics) on priorities, requirements, and sequencing. Develop measurement frameworks, value stories, and reporting approaches to improve comparability, decision-making, and portfolio-level impact.

Lead MyFitnessPal’s product analytics practice and team, advising the Chief Product Officer and senior leaders through actionable data insights. Define product metrics, oversee experimentation, analyze customer and marketing behavior, improve instrumentation and data infrastructure, and shape product strategy, roadmaps, engagement, and retention. This hands-on director role combines executive communication, team development, analytical leadership, and direct analysis across consumer iOS and Android applications.

Leads and develops an analytics engineering team that builds data pipelines, analytical models, reporting, and scalable data infrastructure. Partners with Product, Engineering, Growth, and Operations to deliver reliable, actionable data. Establishes governance, quality, testing, documentation, and modeling standards while prioritizing analytics projects and communicating with stakeholders.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Analyzes healthcare claims, provider, reimbursement, and financial data to support out-of-network dispute negotiations, arbitration, QPA accuracy, and IDR performance. Builds SQL-based reports, dashboards, data processes, metrics, and financial views; validates data quality; integrates internal and external sources; identifies trends and risks; and presents actionable recommendations to leadership and cross-functional stakeholders.

Own end-to-end product and customer analytics across financial product experiences. Define meaningful metrics, analyze complex datasets with SQL, connect customer behavior to business outcomes, and translate qualitative feedback into product insights. Partner with product, engineering, research, data science, and CX teams to influence roadmaps and senior leadership decisions. Use AI tools such as Cursor and Claude to accelerate analysis and automate workflows.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

The Data and Analytics Consultant will design, develop, and implement data reporting, visualizations, and analytics solutions for clients while assisting them in data understanding and value extraction.

Lead design and delivery of Snowflake-centric cloud data platforms. Engage with clients to gather requirements, architect scalable secure solutions, provide technical leadership, develop roadmaps, advise executives, and guide implementation best practices across the analytics stack.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.