VP Applied Data & Analytics

Hertz•Atlanta, GA

163d•$310,000

About The Position

We are seeking a strategic and hands-on Vice President, Applied Data & Analytics to lead the design, development, and deployment of analytics and machine learning solutions that drive measurable business outcomes. This role focuses on the full analytics value chain—from data transformation (ELT) to the development of advanced algorithms and scalable insight delivery across the organization. The ideal candidate brings a strong analytical mindset, deep experience in applied data science, and a proven ability to translate data into business value in complex operations. This role will partner closely with stakeholders across functions to operationalize insights and foster a data-informed culture. The salary range for this position begins at $310,000.

Requirements

15+ years’ experience in Analytics, Data Science, or Business Intelligence; 5+ years in a senior leadership role
Bachelor’s Degree in Data Science, Statistics, Computer Science, Engineering, or related field, required; Master’s Degree or Advanced Technical Degree, preferred
Strong hands-on experience developing and deploying machine learning models and algorithms at scale.
Deep understanding of ELT processes, data modeling, and data product management.
Proficiency in modern analytics technologies (e.g., Python, SQL, DBT, Power BI, Tableau, Snowflake, Databricks).
Strong executive communication skills and ability to influence cross-functional stakeholders.
Ability to collaborate with internal and external stakeholders across multiple functions and locations
Ability to influence
Flexible and adaptable; ability to work effectively in ambiguous situations
Excellent verbal and written communication skills
Results driven, ability to make decisions and help solve problems
Ability to build and lead a diverse, high-performing, results-oriented, and highly-engaged team.
Ability to drive process and organizational change.
Ability to motivate teams and keep a positive attitude in a fast-paced environment.
Ability to work under minimal supervision with a goal-oriented mindset.
Ability to see the big picture and leverage critical thinking and decision-making skills.
Excellent organization, time management, delegation, and prioritization skills.
Courageous leadership and accountability.

Nice To Haves

Experience in Travel, Transport, Hospitality, or Automotive industries, preferred
Master’s Degree or Advanced Technical Degree, preferred

Responsibilities

Lead enterprise ELT (Extract, Load, Transform) strategy to ensure clean, structured, and business-ready data for downstream analytics.
Collaborate with Vice President, Data Architecture and Infrastructure to ensure high availability, quality, and relevance of data assets for consumption.
Own semantic layer design and data product definition in support of enterprise-grade analytics, advanced modeling, and data democratization across the business.
Oversee development and deployment of predictive and prescriptive models (e.g., forecasting, recommendation systems, customer segmentation, optimization).
Identify and prioritize high-impact machine learning use cases in collaboration with functional leaders.
Establish best practices for experimentation, model governance, MLOps, and continuous improvement.
Lead the creation of dashboards, reports, and interactive tools that drive strategic and operational decisions.
Promote consistent KPI definitions and storytelling across the enterprise.
Build reusable analytics assets to enable scale, speed, and reliability in insights delivery.
Partner with commercial, operational, and corporate teams to embed analytics into workflows and business decision-making.
Champion self-service analytics tools and empower business teams with training, documentation, and support.
Drive data literacy initiatives that increase adoption and understanding of insights.
Build, lead, and mentor a high-performing team across data science, analytics, and BI functions.
Develop and execute a strategic roadmap that aligns analytics investments with enterprise priorities.
Promote a culture of experimentation, transparency, and continuous learning.