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About This Role
JOB SUMMARY
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The Audience Campaign Strategist serves as the critical bridge between audience intelligence and campaign execution at The Cokesbury Community. This role translates audience research and segmentation into integrated marketing campaigns across all channels, ensuring that every campaign delivers relevant, personalized experiences that drive engagement, conversion, and customer loyalty. Working closely with the Market Research CRM Strategist, the incumbent orchestrates campaign strategy, builds marketing automation workflows, coordinates cross\-functional execution, and drives continuous performance optimization.
ORGANIZATIONAL CONTEXT \& AREAS OF IMPACT
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Reporting Relationship \& Peers
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Reports to the Chief Revenue Officer, as do:
- Author Partnerships Specialist
- Contact Center Manager
- Creative Content \& Brand Director
- Director, Trade Sales
- Marketing Analytics \& Systems Specialist
Reporting Directly to This Job
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### *EXEMPT*
- Allied \& Amplify Merchandiser
- Digital Marketing \& Social Specialist
Areas of Impact (as applicable)
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### *SALES BUDGET*
- $25M (annual revenue)
### *DEPARTMENT OPERATING BUDGET*
- Campaign execution budgets as authorized by CRO
RESPONSIBILITIES \& SCOPE
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Essential Job Functions
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### *PRIMARY*
- Campaign Strategy Development: Translates audience segments from the Market Research CRM Strategist into comprehensive, multi\-channel campaign strategies. Designs campaign architecture including messaging themes, channel mix, timing strategies, and personalization approaches. Develops campaign roadmaps aligned with business objectives and audience needs. Determines campaign mechanics (segmentation approach, dynamic content strategy, A/B testing plans, and success metrics). Creates integrated campaign plans coordinating email, social media, web, paid media, and content marketing. Partners with the Market Research CRM Strategist to refine audience segments based on campaign performance insights.
- Marketing Automation Implementation: Builds automated customer journeys in Salesforce Marketing Cloud Journey Builder. Develops automation workflows in Automation Studio for triggered campaigns and lead nurturing. Implements segmentation logic and personalization rules in Marketing Cloud. Sets up decision splits, waiting periods, and journey optimization. Creates campaign\-specific subsegments for tactical execution. Configures A/B testing and optimization experiments. Troubleshoots automation technical issues and ensures successful execution. Documents journey logic and automation workflows.
- Campaign Coordination \& Project Management: Manages comprehensive campaign calendar across all channels and audience segments. Coordinates cross\-functional execution across the Digital Marketing \& Social Specialist, Creative Content \& Brand Director, Allied \& Amplify Merchandiser, and Digital Experience \& Content Production Specialist. Establishes project timelines, milestones, and deliverable schedules. Ensures campaign readiness including content, design, technical setup, and deployment preparation. Confirms all Amplify and Ministry Matters email execution is managed by the Allied \& Amplify Merchandiser; deploys the Digital Marketing \& Social Specialist on Amplify/Ministry Matters email only when directed as part of a broader integrated campaign.
### *SECONDARY*
- Campaign Planning Guide Development: Creates strategic planning guides that clearly articulate campaign objectives, audience insights, messaging frameworks, and execution approach. Defines campaign requirements including asset needs, content specifications, and delivery timelines. Establishes success metrics and measurement frameworks for each campaign. Briefs the Digital Marketing \& Social Specialist on technical execution requirements. Documents campaign strategies and maintains campaign knowledge base.
- Performance Monitoring \& Optimization: Monitors real\-time campaign performance across all channels. Analyzes campaign metrics including engagement, conversion, revenue impact, and audience behavior. Provides actionable optimization recommendations. Conducts post\-campaign analysis and shares insights with Market Research CRM Strategist and leadership. Reports on campaign effectiveness to CRO.
*These essential job functions are* *not* *intended to cover all work details or the occasional performance of other tasks as assigned by the supervisor. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential functions of this job.*
Common or Complex Problem\-Solving Challenges
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- Translating Audience Intelligence to Campaign Strategy – Converting complex data segments and behavioral insights into campaign architectures that are both strategically sound and operationally executable.
- Automation Complexity at Scale – Building and maintaining 15–20 simultaneous automated journeys in Salesforce Marketing Cloud without errors, journey conflicts, or data integrity failures.
- Managing Direct Reports Alongside Full Execution Portfolio – Providing effective direction and development for direct reports while carrying a full personal execution load.
- Campaign Calendar Prioritization – Managing competing campaign demands across multiple audience segments, seasonal initiatives, and organizational priorities within a shared production resource environment.
- Year One Infrastructure Build – Establishing campaign workflows, automation architecture, and cross\-functional coordination processes from scratch while simultaneously executing live campaigns.
Authority \& Accountability
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- Authority to make all campaign mechanics decisions including segmentation approach, dynamic content strategy, A/B testing plans, send timing, and channel coordination
- Authority to build, deploy, and optimize automated customer journeys in Salesforce Marketing Cloud
- Authority to direct the Digital Marketing \& Social Specialist and Allied \& Amplify Merchandiser on campaign execution priorities
- Accountability for campaign deployment accuracy, timeliness, and performance outcomes
- Accountability for marketing automation journey quality and optimization
- Accountability for campaign calendar management and cross\-functional coordination
- Accountability for the performance and development of the Digital Marketing \& Social Specialist and Allied \& Amplify Merchandiser
Regular Contacts \& Stakeholders
------------------------------------
### *INTERNAL*
- Digital Marketing \& Social Specialist – direct report; campaign execution direction and Salesforce Marketing Cloud deployment coordination
- Allied \& Amplify Merchandiser – direct report; confirms this role owns all Amplify/Ministry Matters email execution
- Creative Content \& Brand Director – receives Campaign Planning Guides; coordinates on creative brief development and production calendar
- Marketing Operations \& Publishing Liaison – provides advance notice of campaign infrastructure needs
- Digital Experience \& Content Production Specialist – coordinates all page build requests
- Marketing Analytics \& Systems Specialist – receives campaign performance data and analytics support
- Market Research CRM Strategist (CMDR team) – primary partnership for audience intelligence and segment data
- Both Chiefs – Core Strategy Team participation and campaign reporting
JOB REQUIREMENTS
====================
Education, Experience, \& Certifications (as applicable)
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### *REQUIRED*
- Bachelor’s degree in Marketing, Communications, Business, or related field
- Three (3\) to five (5\) years of integrated marketing campaign management experience
- Hands\-on experience building journeys and automation in marketing automation platforms
- Experience with Salesforce Marketing Cloud (Journey Builder, Automation Studio) or similar platforms
- Demonstrated success developing and executing multi\-channel marketing campaigns
### *PREFERRED*
- Salesforce Marketing Cloud certification (Email Specialist, Marketing Cloud Consultant)
- Experience in publishing, education, religious organizations, or mission\-driven environments
- Familiarity with Salesforce Data Cloud and CRM platforms
- HTML/CSS knowledge for email troubleshooting
Knowledge, Skills, \& Abilities
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- Advanced proficiency in Salesforce Marketing Cloud (Journey Builder, Automation Studio, Email Studio)
- Proven ability to translate audience insights into effective campaign strategies
- Data\-driven decision making with strong campaign analytics and performance measurement skills
- Strong project management skills with ability to coordinate multiple campaigns simultaneously
- Cross\-functional collaboration and stakeholder management experience
- People management skills with ability to direct, develop, and support direct reports
- Outstanding written and verbal communication skills
- Knowledge of customer lifecycle marketing and engagement strategies
- Experience with dynamic content and personalization strategies
AI\-Enabled Workplace
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The United Methodist Publishing House is committed to maximizing the impact of artificial intelligence by adopting and integrating AI tools across our work. These technologies are vital to enhancing productivity, fostering creativity, and strengthening decision\-making. All staff members are expected to engage thoughtfully and responsibly with AI as part of their professional responsibilities. Core expectations include:
- Responsible AI Integration – Leverage AI tools strategically to streamline workflows, generate actionable insights, and deliver exceptional outcomes that advance our mission.
- Critical Evaluation \& Oversight – Apply professional judgment and critical thinking when reviewing, refining, and validating all AI\-generated content and recommendations to ensure accuracy and alignment with our standards.
- Continuous Learning \& Innovation – Maintain curiosity and adaptability toward emerging AI technologies, actively contributing to innovative practices within your area of expertise while supporting organizational growth.
- Ethical \& Secure Practice – Uphold UMPH’s commitment to ethical, transparent, and secure AI usage, ensuring all applications align with our mission, values, and community standards.
Equal Employment Opportunity
UMPH is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Salary Context
This $62K-$68K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At United Methodist Publishing House, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($65K) sits 61% below the category median. Disclosed range: $62K to $68K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
United Methodist Publishing House AI Hiring
United Methodist Publishing House has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US. Compensation range: $68K - $68K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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