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About This Role
At Landmark Credit Union, we succeed by putting people first \- and that starts with you. Our culture of inclusion and collaboration enables us to support our members’ financial wellbeing, positively impact the communities we serve, and help our associates grow their careers. Bring your authentic self to work as part of an organization where you’ll feel valued for your unique qualities, are enabled to reach your full potential, and are recognized for your contributions to our success. We strive to ensure you feel empowered to grow and succeed, while also feeling valued and taken care of, as we all do our part to put people first. We invite you to learn more about this and other opportunities at Landmark Credit Union.
NATURE AND SCOPE:
The VP, Analytics and AI is responsible for defining and executing Landmark Credit Union’s enterprise data, analytics, and artificial intelligence strategy to improve member experience, operational efficiency, business decision\-making, and risk management. This position leads the development of modern data and analytics capabilities, including business intelligence, advanced analytics, machine learning, automation, and responsible AI practices. The VP, Analytics and AI partners closely with IT, Digital, Lending, Finance, Risk, Marketing, Operations, and other business leaders to identify high\-value opportunities, deliver actionable insights, and ensure data and AI investments support enterprise strategy, regulatory expectations, and measurable business outcomes. This role reports to the Chief Technology Officer and leads the Enterprise Analytics and AI function, including BI/reporting, data science, and AI/automation teams.
REQUIREMENTS:
1\. Bachelor’s degree required in a quantitative, technical, or related field such as Data Science, Computer Science, Statistics, Mathematics, Economics, Information Systems, or a comparable discipline; advanced degree preferred.
2\. A minimum of 10 years of progressive experience in analytics, data science, business intelligence, data leadership, or related roles, including several years leading enterprise\-level teams and capabilities.
3\. Proven experience defining and executing enterprise data, analytics, and AI strategies that deliver measurable business outcomes, preferably within a regulated industry such as financial services.
4\. Strong understanding of data modeling, analytics techniques, machine learning concepts, modern data platforms, data pipelines, business intelligence tools, and AI\-enabled solutions.
5\. Hands\-on familiarity with modern data and AI tools and technologies, including SQL, Python or R, cloud data platforms, and BI tools such as Power BI or Tableau.
6\. Experience establishing data governance, data quality, privacy, security, model governance, and responsible AI practices in partnership with Risk, Compliance, Audit, Information Security, and business stakeholders.
7\. Demonstrated ability to build, lead, develop, and retain high\-performing analytics, BI, data science, and AI teams.
8\. Strong executive communication, stakeholder management, and change leadership skills, including the ability to present complex data, analytics, AI, value realization, risk considerations, and investment needs to executive leadership and the Board.
9\. Must develop a thorough understanding of company policies and procedures as they relate to this position. Must understand and comply with all job\-related State and Federal laws and regulations.
PRINCIPAL ACCOUNTABILITIES:
1\. Define and execute a multi\-year enterprise analytics and AI roadmap aligned to organizational strategy, digital transformation objectives, and regulatory expectations.
2\. Identify, evaluate, and prioritize high\-impact analytics and AI use cases across member engagement, pricing, fraud and risk management, operations automation, marketing personalization, and other enterprise opportunities.
3\. Partner with IT leadership to design, evolve, and govern modern data platforms, including data lake, data warehouse, real\-time data pipelines, business intelligence tools, and self\-service analytics capabilities.
4\. Establish, own, and mature enterprise data governance policies, data quality standards, stewardship practices, and controls that support privacy, security, compliance, and trusted data usage.
5\. Lead teams responsible for business intelligence, reporting, advanced analytics, data science, AI, and automation, ensuring delivery of actionable insights and decision\-support tools for executives, business leaders, and frontline managers.
6\. Oversee the development of dashboards, models, analytics products, and recommendations that translate complex data into clear narratives, business insights, and measurable actions.
7\. Build and scale AI and automation capabilities, such as recommendation engines, intelligent routing, chatbots, document processing, claims processing, and marketing personalization, to enhance member experience and operational efficiency.
8\. Define and enforce responsible AI practices, including model governance, model monitoring, bias management, explainability, human\-in\-the\-loop controls, and appropriate collaboration with Risk and Compliance.
9\. Serve as the primary analytics and AI partner to leaders across Lending, Retail, Digital, Marketing, Finance, Risk, Operations, and other business areas, helping teams use data and AI to drive better decisions and outcomes.
10\. Communicate analytics and AI strategy, priorities, results, risks, investment needs, and value realization clearly to leadership and governance forums.
11\. Build and lead high\-performing analytics, BI, data science, and AI organizations; attract, develop, coach, and retain talent capable of supporting enterprise needs.
12\. Foster a culture of data\-driven decision\-making, innovation, responsible experimentation, and practical adoption of data and AI across the organization.
13\. Define KPIs and success measures for analytics and AI initiatives and regularly measure and report business impact, including member growth, member experience, efficiency gains, risk reduction, and financial value.
14\. Manage analytics and AI budgets, platforms, tools, vendors, staffing plans, and portfolio priorities to ensure investments deliver measurable value.
15\. Perform other duties as assigned.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LANDMARK CREDIT UNION, 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 $214,900 based on 6,420 positions with disclosed compensation.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LANDMARK CREDIT UNION AI Hiring
LANDMARK CREDIT UNION has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brookfield, WI, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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