Top Remote Analytics Jobs in Ann Arbor, MI

Owns B2B and B2C funnel, enrollment, revenue, and marketing performance reporting. Builds executive and Board dashboards, analyzes CAC, LTV, retention, conversion, spend efficiency, and ROI, and reconciles marketing activity with financial results. Maintains KPI definitions and reporting governance while partnering with Marketing, Finance, FP&A, Sales Operations, and Data teams. Supports strategic planning, diligence requests, and actionable business recommendations through accurate, cross-functional analytics.

Lead insurance risk analytics engagements for property and casualty carriers. Responsibilities include product demonstrations, regulatory filing guidance, pilot and proof-of-concept design, loss ratio and model validation analysis, client project management, discovery sessions, and translating insights for actuarial, underwriting, and executive audiences. The role also develops AI-assisted workflows and provides customer feedback to product teams.

Lead and scale companywide analytics for a DTC marketing business: own marketing measurement, financial forecasting, retention/LTV modeling, data architecture, reporting, and the analytics team. Set analytics strategy, oversee data engineering roadmap, build dashboards, apply statistical and ML methods, and translate insights into executive-level decisions.

Analyzes healthcare claims, provider reimbursement, QPA, and dispute data to support No Surprises Act negotiations, arbitration, and OON liability management. Builds SQL-based reports, dashboards, data processes, and financial views; validates data quality; tracks dispute KPIs; integrates internal and external sources; and presents actionable recommendations to leadership and cross-functional stakeholders.

Analyzes paid media performance across the full funnel, connecting spend and platform metrics to applications, enrollment, student lifetime value, and ROAS. Builds attribution frameworks, Databricks data pipelines, Power BI dashboards, semantic models, and advanced DAX reporting. Supports marketing mix modeling, experimentation, web analytics data quality, forecasting, governance, and workflow automation. Translates complex analytics into strategic recommendations for executive and cross-functional stakeholders.

Leads Relation Insurance’s enterprise data engineering and analytics function, owning data strategy, architecture, pipelines, governance, quality, reporting, and AI-enabled workflows. The role requires hands-on development in SQL, Python, and PySpark, executive communication, technical team leadership, lakehouse architecture, Power BI modeling, production support, and secure data management. The director also directs AI coding agents, builds LLM-powered workflows, establishes engineering standards, and delivers reliable data products aligned with business priorities.

Build and operate trusted finance data products, pipelines, models, reconciliations, dashboards, and automation. Partner with Finance, Accounting, Data Engineering, Product, and Platform teams to resolve data issues, improve reporting, support financial close, establish data lake quality controls, and enable timely insights. Use SQL, Python, dbt, Spark, Airflow, and AI-assisted tools to deliver tested, traceable, production-ready solutions.

Lead and scale Runpod’s analytics and business intelligence function, managing analysts and BI practitioners while setting the analytics roadmap. Own core company metrics, consumption and marketplace analytics, self-service reporting, experimentation practices, and executive and board-level insights. Partner with Product, Finance, Engineering, Security, Supply, and Go-to-Market leaders to improve data quality, modeling, decision-making, and strategic outcomes.

Teaches online undergraduate and graduate data analytics courses, develops syllabi and lesson plans, prepares course materials, facilitates discussions, evaluates student work, provides feedback, and integrates responsible AI practices. The role requires maintaining course alignment with institutional objectives, supporting diverse learners, participating in faculty training, and applying current analytics methods, tools, and ethical data practices.

Build and maintain product data models, ELT pipelines, semantic layers, and self-service analytics assets. Partner with Product Engineering and Data Governance to define canonical data, certify metrics, document taxonomies, monitor data quality, and support expansion and upsell analytics. Translate business requirements into governed data products, communicate insights to technical and non-technical stakeholders, and ensure data tooling complies with federal frameworks including FedRAMP and DoD IL4/5.

Own commercial and growth analytics for GeneDx’s top line, answering questions about volume, revenue, pricing, coverage, field capacity, and product performance. Serve as a senior advisor to commercial and product leaders, structure ambiguous problems, lead analyses end to end using AI agents, communicate executive-ready insights, support evidence-based decisions, monitor outcomes, and surface emerging opportunities. Maintain analytical governance, apply measurement and experimental design, and contribute reusable methods to the analytics function.

Leads operational analytics for the Take 5 Oil Change network, translating transactional and market data into insights that improve sales, profits, volume, and shop performance. Responsibilities include complex analysis, KPI reporting, dashboards, executive presentations, operational initiative measurement, stakeholder collaboration, data-quality improvement, and mentoring analysts. The role requires strong Excel, SQL, financial modeling, communication, and multi-shop operations experience.

Leads analytics for Pricing and Supply, managing a team of approximately eight analysts. Partners with cross-functional leaders to prioritize analytical work, build dashboards and decision frameworks, improve metric quality, and generate insights into pricing, supplier coverage, marketplace performance, conversion, margin, and customer behavior. Coaches analysts, establishes scalable analytics processes, and collaborates with data engineering teams to improve data models, tooling, and self-service capabilities.

Own weekly and monthly sales performance reporting across SPINS, Circana, retailer portals, distributor feeds, and shelf data. Diagnose velocity, distribution, pricing, promotions, and competitive share; identify risks and opportunities; build buyer-facing category and sell-in decks; evaluate promotions and trade ROI; support Costco planning; and develop dashboards and scorecards that turn sales data into actionable insights.

The Senior Manager of Risk Analytics will lead credit risk strategies, manage portfolio performance, optimize decisioning frameworks, and drive business growth through analytics and predictive modeling.

Lead analytics for Wealth Solutions: analyze client segmentation, revenue, and operational data; build models for capacity and workforce planning; produce insights and recommendations for senior leadership; partner with BI and Finance to improve advisor productivity, client experience, retention, and revenue growth; support strategic projects and translate complex data into clear presentations.

Manages Finance analytics engineering and automation, building governed Snowflake and SQL data products, consumption layers, reconciliations, controls, workflows, and integrations. Partners with FP&A, Data Engineering, and business teams to translate Finance requirements into reliable technical solutions. Leads lineage analysis, testing, monitoring, documentation, AI-assisted workflows, production support, and team development while maintaining clear ownership between Finance and enterprise data teams.

The Associate Director will lead AHP’s analytics and data products domain, building curated datasets, transformation pipelines, semantic models, dashboards, and automated reporting. Responsibilities include delivering client and enterprise data products, establishing reporting standards, managing production refreshes and documentation, coordinating data engineering resources, and supporting AI initiatives. The role is hands-on, client-facing, and requires technical leadership across distributed project teams.

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build customer health scores, churn and expansion analytics, retention dashboards, automated alerts, and executive reports. Maintain integrations among databases, CRM, and BI tools while improving data quality and standardizing reporting. Lead AI adoption for analytics, validate generated insights, and enable customer-facing teams to make informed retention and expansion decisions. Requires strong BI, logistics or supply chain analytics, dashboarding, data integration, communication, and customer-focused skills.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Build and maintain HR data pipelines and dbt models on GCP and BigQuery, integrating systems such as Workday and Greenhouse. Develop self-service dashboards, recruiting reports, data validation, governance, and AI-powered analytics experiences. Translate HR and recruiting data into actionable insights, improve reporting automation, and support stakeholders with decision-ready information while helping establish scalable analytics processes.

Own the analytics engineering roadmap, architecture, and production data models. Build and optimize dbt models using SQL, Python, and Databricks; establish dimensional models, governed metrics, testing, documentation, lineage, monitoring, and CI/CD practices. Partner with data engineering, analysts, BI developers, and stakeholders to deliver reliable analytics products, improve performance and cost efficiency, investigate data quality issues, and mentor team members.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.