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About This Role
Job Title: Director of AI Business Operations and Strategy
Company: Houston Chronicle Company
Reports to: President and Publisher
Location: Houston, TX
The Director of AI Business Operations and Strategy will lead the development and execution of enterprise AI initiatives that improve operational efficiency, optimize revenue, accelerate decision\-making, and be accountable to measurable business value. This leader will partner with the Executive Leadership Team to define, refine, and execute the organization’s AI strategy, adoption, plans, KPIs, and impact, and ensure the discipline and rigor required to deliver lasting business value. The role is accountable for the ongoing evolution of the Houston Chronicle’s AI strategy and for translating it into disciplined, measurable execution.
This role owns the AI roadmap, chairs the AI Strategy Committee, represents the voice of the business in AI solution design and deployment, and ensures that AI strategy is aligned , and adopted in a way that delivers long\-term enterprise value.
This is not a technology delivery role. Instead, it sits at the intersection of strategy, business operations, revenue optimization, audience growth, customer experience, and enterprise transformation—connecting AI strategy to the day\-to\-day work of the Houston Chronicle, its business functions, and alignment with Hearst Newspapers’ strategic direction.
The Director will report to the President and Publisher and will work closely with the CRO, VP of Finance, and the Hearst Newspapers Director of AI Business Operations.
Core Responsibilities
- Own and maintain the Houston Chronicle’s AI strategy and roadmap, with clear milestones and success criteria.
- Build a deep understanding of the business problems, workflows, and operational realities at the Houston Chronicle and bring that context into every AI initiative to ensure solutions are grounded in how the business works.
- Partner directly with the Executive Leadership Team to define, refine, and operationalize the company’s top strategic priorities, including responsibility for identifying and implementing 5–7% cost savings through 2027–2028 across :
\- Cost avoidance
- \-Productivity gains
- \-Workflow acceleration
- \-Revenue influence
- \-ROI improvement
- Build and manage a rigorous tracking system to ensure accountability, transparency, and progress across all departments.
- Develop and maintain implementation playbooks, training, and change\-management rigor to operationalize AI at scale, including:
\-Governance frameworks
\-Training materials
\-Adoption strategies
\-Change\-management processes
\-Operational documentation
- Identify workflow redesign opportunities and execution gaps, and intervene to remove bottlenecks, reallocate resources, or recalibrate priorities, improving velocity and embedding AI into day\-to\-day operations.
- Prepare high\-quality briefing materials, analyses, and recommendations for executive decision\-making.
- Serve as the primary liaison between business demand and AI execution by evaluating incoming ideas against other technology investments and recommending prioritization to executives and Hearst Newspapers leadership.
- Lead AI governance, risk, and responsible AI oversight, including:
\-Responsible use
\-Vendor/model evaluation
\-Risk management
\-Legal/reputational considerations
\-Human oversight and operational controls
- Monitor industry trends, emerging technologies, and competitive developments to inform strategy.
- Lead, mentor, train, and grow cross\-functional teams focused on AI strategy and value realization.
- Prepare and present AI strategy updates to the AI Steering Committee.
AI Optimization and Deployment
- Act as the point person for the Houston Chronicle to adopt and effectively utilize Hearst AI tools.
- Identify high\-impact use cases for generative and agentic AI across editorial, advertising, customer experience, and back\-office functions.
- Partner with technology, product, advertising, finance, and editorial leaders to deploy AI tools that enhance productivity, content quality, personalization, and revenue generation.
- Build internal fluency through training, pilot programs, and scaling successful implementations.
Accountability Measures
- Identify and implement 5\-7% cost savings through 2027\-2028\.
Qualifications
- 7\+ years of experience in business operations, strategy, digital media optimization, or a related field.
- Bachelor’s degree.
- Demonstrated ability to drive execution in complex, matrixed organizations.
- Demonstrated success moving initiatives from pilot to enterprise deployment with measurable business outcomes.
- Strong analytical and problem\-solving capabilities, with a bias toward action.
- Proven experience leading cross\-functional initiatives with measurable impact.
- Exceptional communication skills and executive presence.
Preferred
- Experience in business transformation, media, digital publishing, or subscription\-based businesses.
- Experience implementing AI and automation solutions in a business environment.
Key Competencies/Experience
- Required: applied AI execution and direct experience implementing or scaling:
\-AI agents
\-Copilots
\-Workflow automations
\-AI\-assisted operational systems
\-Enterprise AI applications
\-Experience working with platforms and ecosystems such as OpenAI, Anthropic, Google, AWS, and Microsoft.
- Strategic clarity and operational discipline.
- Effective cross\-departmental communication and the ability to influence without direct authority.
- Change management and media transformation.
- High degree of discretion, judgment, and integrity.
About the Houston Chronicle:
The Houston Chronicle, recognized for its Pulitzer Prize\-winning journalism, serves its 2\.3 million residents with content and marketing solutions across two of Houston’s largest websites — HoustonChronicle.com and Chron.com , along with our magazine, television and newspaper brands. Our mission is to spark conversations that inspire action to create a better Houston. All our content and the innovative business solutions we provide invite Houstonians to join conversations about a community we’ve been committed to covering and helping prosper for more than 120 years. The community we all call home.
WHAT WE OFFER:
There has never been a more exciting time to join the Houston Chronicle. Our business is growing and transforming every day. Advertisers are partnering with us to help them navigate new marketing strategies in today’s increasingly complex digital environment. Simply put, the Houston Chronicle is a media company that uses its rich history and data to deliver the most impactful media campaigns for our customers. We leverage our substantial audience reach, coupled with best in class advertising solutions to keep our customer’s brand in front of the right people at the right time.
Why are we different? Our people! Our company is diverse and filled with smart, passionate people who want to make a difference in their community, regardless of the role they play in the company. We offer an upbeat and collaborative working environment and expect our people to challenge the norm.
About Hearst Newspapers:
With 2,500 employees across the nation, HNP encompasses a network of 24 daily and 52 weekly publications, including the San Francisco Chronicle, Houston Chronicle, San Antonio Express\-News and Albany Times Union, several top digital\-only news and lifestyle sites, marketing services businesses, and entertainment businesses such as King Features Syndicate.
At HNP, we are investing in new and innovative ways to tell stories – growing newsrooms, diversifying tools, evolving platforms – to support the millions of people who trust us each month to help them make decisions, take action and be inspired.
*Be a part of something bigger – your headline awaits.*
Recognizing the diverse needs of our candidates, if you are interested in applying for employment and need assistance or an accommodation to use our website or regarding the application process, please contact us by email at [email protected]. Please do not use this email address to inquire about the status of applications. We will only respond to inquiries concerning requests for a reasonable accommodation through this email address.
Degree Level :
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At The Hearst Corporation, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Hearst Corporation AI Hiring
The Hearst Corporation has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Houston, TX, US, New York, NY, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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