Top Remote Analytics Jobs in Detroit, MI

Design, build, and maintain scalable data systems and ETLs on GCP; connect production data to business systems; generate analytics and ML-driven insights for GTM teams; work cross-functionally and introduce modern tools (Airflow, DBT, Presto, Hightouch).

Lead and own the population health analytics pipeline: intake and reconciliation of claims, eligibility, and MAO-004 data; identify and reconcile care gaps and closures; deliver practice-level performance analysis and billing reconciliations; build funnel instrumentation; document, standardize, and automate pipelines with Product and Engineering. Ensure billing-grade accuracy and support program measurement aligned to CMS reconciliation windows.

Lead product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.

Lead end-to-end analytical initiatives on partner enrollment funnels, ensure data quality, build reporting pipelines with BI/engineering, use AI-assisted tools to accelerate analysis, translate insights into strategic recommendations for leadership, and coach growing analytics team members.

Design and implement scalable data platforms, build ETL/ELT pipelines, integrate multiple data sources, define KPIs, create dashboards and executive reports, ensure data quality/governance, and collaborate with stakeholders to optimize BI capabilities.

Lead analytics strategy, governance, and reporting to create trusted metrics. Drive insights across churn, retention, product telemetry, and marketing. Build analytical models, oversee Looker BI, partner with Data Engineering, establish data quality and AI-readiness, and hire and grow the analytics team to influence leadership decisions and business outcomes.

Build and maintain production data pipelines and foundations for People Analytics and AI-driven tools. Integrate HR systems (Workday, Greenhouse), design data models, ensure governance for sensitive employee data, support data science and reporting, and deliver dashboards and Streamlit apps. Optimize queries, enable LLM consumption with quality and latency controls, and collaborate cross-functionally to transition AI prototypes to production.

Lead product analytics for Guest or Host App: analyze large datasets, design and evaluate experiments, build metrics and reporting tools, apply statistical and causal methods, partner cross-functionally, present insights to stakeholders, and leverage AI to automate tasks.

Six-month internship to collect, clean, and analyze technical service, lab, and customer case data; build structured datasets; create visualizations and Power BI dashboards; support AI-assisted data processing and automation; present findings and collaborate with engineers and scientists to inform product and reliability decisions.

Lead analytics partner to Community Support, driving data-driven roadmaps, scalable reporting and measurement, causal inference and experimentation, and improvements to agent/guest/host experiences via analytics, modeling, and stakeholder influence.

Lead the RevOps analytics function: manage an analytics team, own revenue reporting and forecasting, build measurement frameworks, enforce data governance, partner with BI/engineering, analyze funnel and retention metrics, and embed AI/automation to improve revenue predictability and GTM performance.

Lead analytics for law firm operations by writing advanced SQL and Python/R scripts, building executive dashboards, standardizing KPIs, analyzing case lifecycle metrics, identifying bottlenecks, and delivering actionable recommendations to drive capacity planning and process improvements.

The Data & Analytics Architect will design and implement analytics solutions, support client engagements, and lead data architecture projects, requiring extensive technical and consulting skills.

Lead the Payments Insights Analytics team, utilizing data and AI/ML for strategy execution, operational excellence, and stakeholder communication.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Own reconciliation and validation of client billing across internal systems, build repeatable data-quality checks and documentation, support Client Group with reporting and dashboards, investigate discrepancies, and drive process improvements and automation to reduce billing friction.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Manage client data and analytics projects to design and implement data strategies, automation, visualizations, and predictive analytics. Translate data into actionable insights, lead cross-functional stakeholder engagement, scope and monitor budgets, and develop internal tools and templates. Apply finance/CPA knowledge where relevant and deliver client-facing presentations.

Lead analytics strategy and delivery for healthcare clients: gather reporting requirements, design and build enterprise data platforms (Databricks/lakehouse), develop and validate reports (including Epic reporting), influence decentralized data teams, and provide post-implementation support and modernization.

Lead analytics and data-product initiatives for healthcare clients: gather reporting requirements, design and develop reports/products, test and validate, provide post-implementation support, and partner with stakeholders to drive adoption and measurable value.

Lead-level analyst who designs, develops, and delivers reports, dashboards, and data solutions (SQL, Power BI, SSRS, Excel). Analyzes complex healthcare, financial, and operational data, ensures data quality and ETL processes, mentors analysts, manages analytics projects, drives automation and process improvements, and presents insights to leadership and stakeholders.

Lead identification and quantification of issuance leakage across acquisition, application, approval, and funding funnels. Analyze digital customer journeys and conversion gaps, run root-cause diagnostics, design experiments, build executive dashboards, and translate findings into prioritized, quantified business recommendations partnering with Product, Marketing, Credit Risk, Operations, and Engineering.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

Own product telemetry, maintain dbt models in Snowflake, support self-service reporting and NLP queries, drive product insights from user and retention data, and monitor Airbyte syncs to ensure data quality.

Lead design and implementation of data models, write optimized SQL at scale, enable semantic layers and AI tooling, manage analytics infrastructure, build frontend data surfaces, and partner with product and engineering to structure source systems for reporting.