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About This Role
Summary:
The Manager, Advanced Analytics will report to the Director, Advanced Analytics and will lead developing advanced analytics, building Machine Learning models, and delivering strategic insights that drive enterprise wide decision\-making. This role will partner in developing analytical roadmaps, partner with senior leaders across Customer Experience \& Operations, IT, Marketing, Pricing, Finance, and the Existing Customer business, and provide data backed recommendations that influence revenue growth, customer retention, pricing strategies, and operational performance. The Manager will own the design and execution of scalable analytical frameworks—including Customer Lifetime Value, segmentation, test and learn programs, and subscriber trend modeling—to enable fast, data driven prioritization across the organization. Success in this role will be defined by elevating analytic maturity, shaping cross functional strategy, and enabling business units to make informed decisions that materially impact profitability, customer experience, and long\-term value creation.
Duties and Responsibilities:
- Lead development of Advanced Data science (DS) and Machine Learning (ML) models across Customer care, pricing and other enterprise groups.
- Explore and develop new high value use cases to drive enterprise growth using DS and ML techniques.
- Lead the development and optimization of enterprise pricing, forecasting, and customer value models, partnering with Pricing and Finance to shape strategic pricing decisions and long\-term revenue plans.
- Oversee cross functional execution of price, offer, and customer communication changes, ensuring alignment between Pricing, IT, Marketing, and Customer Experience.
- Own the analytical strategy and roadmap that drives Customer Lifetime Value growth, using advanced analytics to identify opportunities across upgrades, collections, proactive outreach, and support channels.
- Guide and govern existing customer segments and campaign pricing, including offer strategy, commercial guardrails, execution governance, and performance measurement.
- Direct a robust test and learn agenda, including experiment design, funnel optimization, and performance evaluation to accelerate customer growth and operational efficiencies.
- Oversee enterprise analysis of subscriber trends, lifecycle patterns, and customer journey performance, partnering with Customer Experience leaders to design best in class, Churn treatments
- Drive segmentation strategy and implementation, ensuring segments, propensity models, and personalization frameworks are fully leveraged across customer retention, pricing, loyalty, communications.
- Lead the transformation of raw data into trusted, high quality analytical assets, ensuring governance, validation, and the delivery of compelling visualizations and executive storytelling artifacts.
- Provide leadership, coaching, and direction to analysts, fostering analytical excellence, business acumen, and strong cross functional partnership capabilities.
- Represent Cust Care Analytics in senior level forums, influencing decisions that impact revenue, customer experience, churn reduction, operational efficiency, and enterprise prioritization.
- Drive continuous improvement across analytics processes, identifying opportunities for automation, better data infrastructure, and scalable insights delivery.
- Manage and balance multiple high value projects with optimism and can\-do will\-do attitude
- Perform additional strategic duties as assigned to support enterprise growth initiatives.
Skills and Competencies:
- Advanced proficiency in SQL, Python, database structures, and modern BI/visualization platforms, with the ability to set analytical standards, guide best practices, and mentor analysts in tool adoption.
- Early adopter of AI technologies. Self\-driven learner that propagates learnings across the organization to elevate all.
- Deep working knowledge of data science, machine learning techniques, and statistical modeling, enabling governance over model development, validation, and enterprise level deployment.
- Ability to translate complex analytical outputs into executive ready insights, shaping cross functional strategy and influencing financial, pricing, and customer experience decisions.
- Demonstrated ability to lead, coach, and elevate analysts, fostering a culture of excellence, curiosity, and high\-quality output across all analytical deliverables.
- Highly effective cross functional collaborator, with the ability to influence senior stakeholders across Customer Experience, Pricing, IT, Marketing, and Finance.
- Skilled at driving alignment across matrixed teams, setting governance frameworks, and ensuring consistent interpretation of customer insights across the organization.
- Exceptional written and verbal communication skills, capable of producing executive level storytelling, data visualizations, and strategic recommendations that guide enterprise decisions.
Ability to simplify complexity and communicate analytical implications clearly to nontechnical audiences.
- Demonstrates a strong commitment to personal and professional development, proactively pursuing new analytical techniques, emerging technologies, and leadership capabilities.
- Self\-directed learner who seeks out stretch opportunities, challenges the status quo, and continuously strengthens business knowledge and technical depth.
- Actively incorporates feedback, reflects on opportunities to improve, and invests in developing managerial maturity and enterprise leadership skills.
- Strong ability to manage multiple high value projects concurrently, maintaining accuracy, timeliness, and quality in a fast\-paced environment.
Minimum Qualifications:
- Bachelor’s degree in analytics, finance, statistics, economics or other related field of discipline or equivalent experience.
- 5\-7\+ years in data analytics within finance and/or GTM customer management, preferably within retail security or subscription\-based business; both business\-to\-consumer and business\-to\-business.
- Demonstrated expertise with SQL, Python, Tableau, MS Office, Salesforce, database management, and cloud platform data environments, with proven ability to oversee and govern analytical standards across teams.
- Experience influencing business decisions in highly matrixed organizations, with a track record of partnering cross functionally to drive strategic outcomes.
- Advanced strategic thinking and problem\-solving capabilities, with the ability to design and govern analytical roadmaps, pricing frameworks, and customer value models aligned to enterprise goals.
- Master’s degree (MBA, MS Analytics, Statistics, Economics, or related quantitative discipline).
- Experience leading analysts or cross functional project teams, demonstrating the ability to coach, mentor, and elevate analytical talent.
- Hands on experience deploying machine learning models at scale, including model governance, validation, and performance monitoring.
- Exceptional written and verbal communication skills, including the ability to craft executive level narratives, insights, and recommendations.
- Demonstrated commitment to continuous learning and self\-driven development, including proactively expanding technical depth, business acumen, and leadership capabilities.
Preferred Qualifications:
- Master’s degree (MBA, MS Analytics, Statistics, Economics, or related quantitative discipline).
- Experience leading analysts or cross functional project teams, demonstrating the ability to coach, mentor, and elevate analytical talent.
- Hands on experience deploying machine learning models at scale, including model governance, validation, and performance monitoring.
Communication Skills:
- Writing, Talking/Hearing on the phone (Continually\=67\-100% of workday)
Environment Requirements:
- Hybrid min 4 days in office at Boca Raton, FL and home
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 ADT, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
ADT AI Hiring
ADT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boca Raton, FL, 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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