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Executive Predictive Analytics information

What is an executive predictive analytics?

Executive Predictive Analytics refers to the use of advanced data analysis techniques and machine learning models by organizational leaders to forecast future business outcomes and inform strategic decisions. Executives use predictive analytics to anticipate market trends, identify risks and opportunities, and optimize resource allocation. This role requires a combination of business acumen, data science knowledge, and the ability to translate complex data into actionable insights for high-level decision-making.

How does an executive predictive analytics professional typically collaborate with other departments to drive business outcomes?

An Executive Predictive Analytics professional often works closely with teams across marketing, finance, operations, and IT to align advanced analytics initiatives with broader business goals. They translate complex data insights into actionable strategies, facilitating data-driven decision-making at the executive level. Regular cross-functional meetings and workshops are common to ensure that predictive models are integrated into business processes and that stakeholders understand their impact. Collaboration is key, as these executives must communicate technical findings in an accessible way to influence strategic planning and organizational change.

What are the key skills and qualifications needed to thrive as an executive in predictive analytics, and why are they important?

To thrive as an Executive in Predictive Analytics, you need advanced expertise in statistical analysis, data modeling, and business strategy, usually supported by a degree in data science, statistics, or a related field. Familiarity with analytics platforms such as SAS, R, Python, and big data tools, as well as certifications like Certified Analytics Professional (CAP), is highly beneficial. Exceptional leadership, communication, and strategic decision-making abilities set standout executives apart in this field. These skills enable leaders to drive data-informed organizational growth, align analytics initiatives with business objectives, and foster innovation across teams.

What is the difference between Executive Predictive Analytics vs Data Scientist?

AspectExecutive Predictive AnalyticsData Scientist
Required CredentialsOften requires advanced degrees in business, analytics, or related fields; certifications in analytics toolsTypically requires degrees in computer science, statistics, or mathematics; certifications in programming and data analysis
Work EnvironmentStrategic, executive-level settings; focuses on business impact and decision-makingTechnical environment; involves data modeling, coding, and statistical analysis
Employer & Industry UsageUsed in corporate strategy, finance, marketing, and operations departmentsEmployed across tech, finance, healthcare, and research organizations

While both roles involve data analysis and predictive modeling, Executive Predictive Analytics focuses on strategic insights for leadership decision-making, whereas Data Scientists handle technical data modeling and algorithm development. The roles often overlap but differ mainly in scope and target audience.

What cities in Michigan are hiring for Executive Predictive Analytics jobs?

Cities in Michigan with the most Executive Predictive Analytics job openings:

Executive Predictive Analytics jobs near you

Director, FCSD Data Analytics and Business Intelligence

Allen Park, MI • On-site

Ford Motor Company
Motor Vehicle Manufacturing • 10K+ employees
Learn more about Ford Motor Companyexternal

Full-time

Re-posted 15 days ago

Ford Motor Company rating

7.6

Company rating: 7.6 out of 10

Based on 529 frontline employees who took The Breakroom Quiz

Job description


In this role, you will lead the strategic integration of several key functions-including data strategy, business intelligence, reporting, and customer experience analytics-into a unified, global team comprising both internal employees and external partners. Operating as a proactive service function, your organization will work in partnership with FCSD's business line leaders to directly support and accelerate their business results. This role is not about maintaining legacy processes or acting as a reactive "data vendor" that extracts raw files and distributes spreadsheets. Instead, you are tasked with architecting a modern, AI-first insights engine. You will build a highly proactive team that pushes our business leaders on their strategic thinking, challenges status-quo assumptions with deep, diagnostic and predictive analytics, and unlocks the critical insights needed to drive our business transformation.
Responsibilities
1. Future-State Operating Model & Resource Integration
  • Design the Future Organization: Integrate diverse reporting and data/analytics teams into a global structure of 30+ professionals.
  • Orchestrate the Employee-Vendor Ecosystem: Efficiently manage a blended team of internal talent and external vendors. Ensure our partners effectively "run the railroads" so that internal teams can focus on high-value strategy and proactive insight generation.
  • Lead Cultural and Skill Transformation: Have a mindset of proactive, strategic advisory partner to FCSD business leaders, enabling the team to work seamlessly in modern, cloud-native, and AI-first environments.

2. Proactive Business Intelligence, Insights & Service Delivery
  • Support Business Line Results: Position the Data Analytics and Business Intelligence team as a premier service function, collaborating intimately with FCSD business line leaders to drive EBIT expansion and better operational execution.
  • Challenge the Status Quo: Shift the division away from, manual spreadsheet-sharing toward dynamic, predictive business intelligence. Empower your team to synthesize complex datasets and deliver sharp, actionable insights that proactively shape future business strategies.
  • Establish Dynamic Enterprise KPIs: Oversee the development and automation of next-generation performance metrics, utilizing AI-driven visualization to track and predict customer pay service, leads, wholesale, parts supply & logistic, network performance.
  • Own the Semantic Layer and Metric Governance: Reinforce centralized Business Rules to eliminate conflicting data across business units, ensuring a single version of truth.

3. Integrated Customer & Dealer Data Strategy
  • Consolidate Dealer & CX Data: Evolve the customer and dealer lifecycle by bringing Dealer Management System (DMS) datasets and Customer Experience (CX) survey metrics into a single, unified data strategy.
  • Synthesize Voice of Customer (VoC) and industry data: Build cohesive feedback loops that make customer and dealer sentiment and industry benchmark insights highly visible and actionable from executive leadership to the dealership floor.
  • Continue Leadership in Data Integration: Define the long-term vision for dealer-partner data sharing, integration, and security, ensuring high-integrity, compliant pipelines feed the team's advanced analytics engines.

4. AI-First Analytics & Modern Tech Stack Evolution
  • Establish an AI-First Culture: Leverage AI to automate routine reporting, accelerate insight generation, and perform diagnostic modeling at scale.
  • Partner with Central Ford Tech teams: Collaborate closely with Ford's centralized data science and Enterprise Technology organizations to transition FCSD's data assets from legacy environments into modern, cloud-native architectures

Qualifications
Required Skills and Capabilities
  • Transformational Leadership & Vision: Exceptional capability to lead global, cross-functional teams through change, while maintaining a high-performing service identity.
  • AI-First Technical Vision: A forward-looking leader with a strong conceptual grasp of generative AI, large language models, automated machine learning, next-generation visualization platforms, and modern cloud data ecosystems. Must keep pace with new innovation and leverage the latest tooling and agents to rapidly prototype solutions and prove value before scaling.
  • Proactive Advisory & Influence: A track record of acting as a consultative partner rather than a transactional service provider; someone who can leverage data to challenge senior executive thinking and constructively shape business outcomes.
  • Collaborative Relationship Builder: Strong interpersonal skills with a proven track record of partnering across corporate skill teams, technology partners, and external organizations.
  • Resource Orchestration: Ability to manage complex, blended teams (internal and external vendors) and design high-performing operating models during organizational change.
  • Action & output oriented. Hands on keys builder that can roll up their sleeves alongside engineering teams.
Minimum Qualifications:
  • Education: Bachelor's degree in Business Analytics, Data Science, Statistics, Economics, Engineering, Mathematics, Business, or a related field.
  • Experience:
    • Minimum of 10 years of progressive experience in business analytics, data strategy, business intelligence, or customer insights.
    • Minimum of 5 years of experience leading and developing global, multi-tiered, or cross-functional teams of 20+ professionals (including direct employees and vendors).
    • Experience leading a technology-focused team through transformation
  • Technical & Platform Familiarity: Deep conceptual knowledge of modern cloud environments (e.g., Google Cloud Platform), automated BI semantic layers, advanced analytics workflows, and AI agents with experience successfully migrating legacy reporting into modernized platforms.
Preferred Qualifications:
  • Advanced Degree: Master's degree in Data Science, Mathematics, Engineering, or an MBA with an analytical focus.
  • Automotive or Aftersales Experience: Prior experience in dealership operations, automotive parts and service, or aftersales business dynamics.
  • Modern Methodology Certifications: Certified in modern, agile data product management, continuous delivery frameworks, or Lean methodologies.

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