Top Remote Machine Learning Jobs in Detroit, MI

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Designs, develops, deploys, and supports complex enterprise data pipelines, data models, ETL/ELT processes, and analytical solutions. Leads data governance engineering initiatives involving metadata catalogs, lineage, data quality, and remediation. Builds governed, AI-ready datasets and integrates AI capabilities into production data workflows while validating performance, security, privacy, and reliability. Collaborates with business, technology, data science, and application teams to deliver scalable, compliant solutions.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Supports energy and sustainability analytics for data center clients by cleaning, validating, and analyzing datasets; developing Power BI or Tableau dashboards, dataflows, and reports; documenting methodologies; performing quality checks; and delivering accurate outputs across concurrent projects. Collaborates with energy and sustainability teams, learns internal analytics platforms, and identifies opportunities to improve reporting, automation, and workflows.

Leads the strategy, architecture, implementation, governance, and commercialization of the enterprise data platform. Builds cloud data infrastructure, pipelines, models, DataOps practices, audience intelligence capabilities, and privacy-safe data products. Partners with executive and cross-functional teams to support analytics, personalization, AI, machine learning, reporting, and revenue opportunities. This hands-on technical leader also manages strategic vendors, drives enterprise data initiatives, and communicates technical strategy to non-technical stakeholders.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages sprint planning, releases, DevOps metrics, project delivery, and stakeholder communication. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, and ensures reliable, well-documented data solutions that support organizational reporting and analytics needs.

Manages a business analytics team that delivers insights, KPI reporting, trend analysis, data visualizations, governance, and actionable recommendations. Guides statistical analysis, data modeling, querying, experimentation, and self-service analytics while aligning practices with enterprise standards. Partners with leaders to identify opportunities and risks, improve data quality, drive analytics adoption, and support strategic and operational decisions. Oversees team performance, department projects, communications, and best-practice development.

Leads global people analytics and workforce intelligence strategy, building AI-driven tools, predictive models, dashboards, and decision-support applications. Partners with IT, data, and security teams on governance, privacy, and ethical AI. Advises executives, democratizes workforce insights, measures people-program ROI, and anticipates talent trends through benchmarking and workforce modeling.

Leads business analytics engagements, translating stakeholder needs into data requirements, models, dashboards, forecasts, statistical analyses, and actionable recommendations. Performs data exploration, querying, modeling, and visualization across large datasets; supports data governance, quality, security, and stewardship. Mentors analysts, communicates insights to leaders, collaborates with enterprise data teams, and identifies opportunities to apply machine learning in business decision-making.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Own analytics for Root’s Customer Experience domain and policyholder retention. Develop metrics, analytical frameworks, experimentation systems, and strategic recommendations. Partner cross-functionally with Product, Engineering, Data Science, and Operations to shape roadmaps, evaluate customer initiatives, quantify business impact, and influence senior leadership decisions through rigorous analysis and clear communication.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.

Leads enterprise advanced analytics and modeling to identify drivers of business performance across brands, markets, customers, accounts, and geographies. Translates findings into prioritized actions, playbooks, launch strategies, and scenario-based recommendations. Oversees rigorous, scalable analytical approaches, partners with senior stakeholders, and develops analytics talent across the organization.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Leads commercial analytics and business intelligence strategy, including executive dashboards, account reporting, revenue and pricing analytics, operational KPIs, forecasting, segmentation, and predictive modeling. Partners with Sales, Finance, Marketing, Operations, and Product to improve revenue, profitability, customer retention, and marketplace performance. Presents insights to executives and customers while managing analytics teams, budgets, vendors, and BI scalability.

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.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Collects, processes, and analyzes data from multiple sources using R, Python, and SQL. Develops reports, visualizations, analytical methods, and data-driven insights while applying statistical and introductory machine learning techniques. Collaborates with internal and external stakeholders, documents project progress in JIRA, debugs code, clarifies requirements, and communicates findings through reports and presentations.

Designs, builds, tests, documents, and maintains large-scale data pipelines across platforms and sources. Performs ETL and ELT, develops data quality and governance processes, supports anomaly detection, and ensures database security and compliance. Collaborates with architects, analysts, and stakeholders to translate business needs into technical solutions. The role also mentors junior engineers, oversees projects, and works with cloud platforms, relational databases, SQL, Python, Spark, and large-scale processing engines.

Analyze telematics, IoT, sensor, and firmware data to evaluate hardware performance, reliability, and field issues. Build dashboards and reports, identify trends and anomalies, apply statistical and machine learning techniques, validate firmware improvements, and provide actionable insights to engineering, product, data science, and customer support teams. Help develop scalable data pipelines and promote data-driven hardware and software development.

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.

Lead design and implementation of enterprise fraud prevention strategy and decisioning engines for loan products. Use data analytics and machine learning to assess fraud and identity risk, recommend verification standards, set KPIs, report to senior leadership, and lead/mentor a team to automate and improve fraud prevention while maintaining a frictionless customer experience.

Establish and lead the People Analytics function, building trusted workforce data foundations, governance, privacy standards, analytical products, workforce planning capabilities, and AI-enabled decision support. Partner across HR, Finance, IT, and business leadership to deliver executive insights, improve workforce decisions, and scale analytics operations. Recruit and develop the analytics team while ensuring responsible data use, global-local compliance, stakeholder adoption, and alignment with organizational priorities.