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About This Role
The Manager or Senior Manager, Consumer Marketing Data Science & Strategy serves as a strategic analytics partner to Consumer Marketing leadership, driving data-informed decision making across customer acquisition, activation, engagement, and conversion initiatives. This role combines advanced analytics, business strategy, and stakeholder management to solve complex business challenges and unlock growth opportunities across HGV's sales & marketing portfolio.
The successful candidate will lead high-impact analytical initiatives, influence business strategy through actionable insights, and manage a combination of internal and outsourced analytics resources.
Here’s why you will love it here:
- Recognition Programs and Rewards
- Excellent health care options, including medical, dental, and vision
- A people-first culture
- Go Hilton: Travel Discounts Program Hilton hotel rates worldwide.
- Perks at work: Employee Pricing platform
- Employee Assistance Program that supports your physical and mental well-being.
- Paid Vacation Time and Paid Sick Days
- 401(k) program with company match
- Tuition reimbursement programs
- Numerous learning and advancement opportunities
- And more!
The Manager or Senior Manager, Consumer Marketing Data Science & Strategy serves as a strategic analytics partner to Consumer Marketing leadership, driving data-informed decision making across customer acquisition, activation, engagement, and conversion initiatives. This role combines advanced analytics, business strategy, and stakeholder management to solve complex business challenges and unlock growth opportunities across HGV's sales & marketing portfolio.
The successful candidate will lead high-impact analytical initiatives, influence business strategy through actionable insights, and manage a combination of internal and outsourced analytics resources.
Position Summary:
Strategic Business Partnership
- Partner closely with Marketing, Sales, Inventory, Operations, and executive stakeholders to understand business objectives, challenges, risks, and operational constraints.
- Translate ambiguous business questions into structured analytical frameworks and actionable recommendations.
- Serve as a trusted advisor to business leaders by providing fact-based insights that inform strategic and tactical decisions.
- Lead discussions with stakeholders to align on project objectives, methodologies, deliverables, timelines, and success metrics.
Data Science & Analytics Leadership
- Lead the design, execution, and communication of advanced analytical solutions that drive measurable business impact.
- Develop and apply statistical modeling, predictive analytics, customer segmentation, forecasting, experimentation, and optimization methodologies.
- Identify opportunities to improve marketing effectiveness, conversion performance, customer engagement, and operational efficiency.
- Deliver clear and compelling insights through executive-ready presentations, dashboards, and business recommendations.
- Ensure analytical rigor, data quality, and sound methodology across all projects.
Project & Team Leadership
- Operate as a player-coach by independently leading complex analytical projects while also managing broader workstreams across the team.
- Provide guidance, prioritization, and quality oversight for outsourced Data Science & Strategy resources.
- Manage project portfolios, balancing strategic initiatives with ongoing business support.
- Establish best practices, analytical standards, and scalable processes for project execution and delivery.
- Mentor junior analysts and data scientists to strengthen technical and business acumen.
Cross-Functional Collaboration
- Partner with DbM, MarTech, IT and Operations teams to enable effective data-driven solutions.
- Drive alignment among stakeholders with varying priorities and perspectives.
- Facilitate communication between technical and non-technical audiences to ensure understanding and adoption of analytical recommendations.
Required Qualifications:
- Bachelor’s degree in Data Science, Statistics, Economics, Mathematics, Business Analytics, Marketing Analytics, Computer Science, or a related quantitative discipline.
- At least 5 years of relevant experience for the Manager level or 8 years for the Senior Manager level in analytics, data science, strategy, consulting, or a related field.
- Demonstrated experience leading analytical initiatives from problem definition and solution design through implementation and measurement of business impact.
- Strong analytical capabilities, with proficiency in SQL and Python and working knowledge of Microsoft Azure, cloud-based data platforms, and Databricks.
- Experience with business intelligence and data visualization tools such as Tableau, Power BI, or Looker.
- Strong stakeholder management and communication skills, with demonstrated experience presenting analytical insights and recommendations to senior leadership.
- Demonstrated ability to manage competing priorities and deliver high-quality work in a fast-paced, evolving environment.
Preferred Qualifications:
- Experience supporting consumer marketing, customer lifecycle management, CRM, loyalty, hospitality, travel, timeshare, or a related industry.
- Experience applying analytics to customer acquisition, activation, conversion, retention, or marketing channel performance.
- Experience leading outsourced, offshore, or matrixed analytics teams and resources.
- Advanced degree, such as a master’s degree or MBA, in a quantitative or business-related discipline.
Leadership Competencies
- Strategic thinking and strong business acumen.
- Consultative, collaborative, and solution-oriented approach.
- Ability to influence decisions and drive alignment without direct authority.
- Strong project management, prioritization, and execution skills.
- Comfort operating effectively amid ambiguity and evolving business needs.
- Proven ability to balance hands-on analytical execution with team leadership, mentorship, and coaching.
Success Measures
In this role, success will be measured by:
- Delivery of actionable insights that drive measurable business results.
- Stakeholder satisfaction and adoption of analytical recommendations.
- Quality, timeliness, and scalability of analytical solutions.
- Effective management and development of internal and outsourced resources.
HGV was awarded four out of five stars for fostering a culture of work-life balance and family-friendly benefits, and is one of Newsweek's "America's Greatest Workplaces for Parents & Families." Over the past 30 years, the highest honor we’ve received is the continued loyalty of our Owners, Members and Guests. We’ve also earned accolades for the remarkable work of our dedicated Team Members throughout our resorts, call centers and corporate offices.
We offer an excellent benefit package to our full-time Team Members that include Day One medical, dental and vision insurance, 401K plan, Paid Time Off (PTO) program and extraordinary travel benefits!
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Hilton Grand Vacations, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Hilton Grand Vacations AI Hiring
Hilton Grand Vacations has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orlando, FL, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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