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About This Role
Overview:
National Debt Relief is looking for a Senior Analyst, Conversational AI Analytics to join the Digital \& CX analytics team. This role sits at the intersection of data, user experience (UX), and Agentic AI \- you will be the analytical engine behind our conversational AI experiences, translating how real users interact with our voice and chat AI into insights that shape better conversations, smarter flows, and stronger outcomes.
You are not the engineer who builds AI Agents, but you are essential to making them work well. You will own the measurement framework for our conversational AI products \- defining what good looks like, tracking where experiences break down, and partnering with designers, product managers, and our Applied AI team to close the gap between current performance and the ideal user journey.
This is a high\-impact, cross\-functional role with direct exposure to senior leadership. The right person brings both analytical rigor and a genuine curiosity for how people communicate with AI.
Responsibilities:
Essential Duties/Responsibilities Conversational AI Analytics \& Measurement* Own the measurement framework for conversational AI products \- defining KPIs across containment, resolution, deflection, intent recognition accuracy, conversation completion rates, drop\-off, and sentiment.
- Build and maintain dashboards that surface conversation health metrics, user behavioral patterns, and performance trends across voice and chat channels.
- Provide UX insights via analysis of conversation transcripts, dialogue flows, and session data to identify friction points, misunderstood intents, fallback loops, and abandonment patterns.
- Partner with the Applied AI team to establish baseline performance benchmarks and define what success looks like for each conversational initiative.
- Translate raw interaction data into clear, actionable recommendations for conversation designers, product managers, and ML teams.
UX Research \& User Insight* Conduct mixed\-methods research \- combining quantitative conversation analytics with qualitative usability testing, session replays, and user feedback \- to build a complete picture of user experience quality.
- Map conversation flows against actual user journeys to identify where AI\-guided paths diverge from user expectations or intent.
- Support the design and analysis of A/B tests on conversational flows, dialogue variations, and escalation triggers.
- Synthesize research findings into user journey documentation, annotated flow diagrams, and prioritized insight reports for product and design stakeholders.
Cross\-functional Collaboration \& Communication* Serve as the data\-informed voice in conversation design reviews \- bringing evidence from real user interactions to design critiques, sprint reviews, and roadmap discussions.
- Present insights and performance reports to senior stakeholders in clear, non\-technical language that connects conversational metrics to business outcomes (conversion rates, customer satisfaction, operational cost savings).
- Partner with engineering to ensure proper logging, tagging, and event tracking are in place to support robust conversation analytics.
- Collaborate with the broader product organization to align conversational AI metrics with company\-wide data governance and reporting standards.
Continuous Improvement* Establish a regular cadence of conversation audits \- systematically reviewing transcript samples to surface emergent issues before they appear in aggregate metrics.
- Monitor model and flow performance post\-deployment, flagging regression or drift and coordinating response with the AI and engineering teams.
- Stay current with trends in conversational AI, NLP evaluation methods, and UX analytics tooling \- and identify where new capabilities can strengthen the team’s analytical practice.
Current Initiatives (as of 2026\) The Applied AI team is currently focused on the following \- these represent the starting portfolio, not the boundaries of the role:* Voice \- Measuring and improving the user experience of an AI\-powered voice bot, including turn\-level analysis, intent accuracy, and escalation/hand\-off optimization.
- Conversational Chat \- Tracking and improving chatbot performance across the homepage, landing pages, and key customer journeys from a UX and outcomes lens.
- Internal Knowledge Base Tooling \- Supporting adoption and usability measurement for AI\-powered internal tools through employee feedback loops and query\-level analytics.
Qualifications:
Education/Experience* 4\+ years of experience in UX analytics, product analytics, or a related data\-focused role, with at least 1–2 years working on conversational AI, chatbot, or voice AI products.
Required Skills/Abilities* Demonstrated ability to analyze conversation\-level data \- transcript review, intent classification accuracy, fallback rates, CSAT/sentiment scoring \- and turning findings into design or product recommendations.
- Proficiency with analytics and BI tools (e.g., Tableau, Looker, Power BI, or similar) for building dashboards and self\-serve reporting.
- Comfortable working with SQL to query conversation logs and event data; Python or R a plus but not required.
- Strong understanding of UX research methods \- usability testing, user journey mapping, qualitative coding of transcripts, and mixed\-methods synthesis.
- Familiarity with core conversational AI concepts \- intents, entities, dialogue management, NLU/NLP, fallback handling, escalation logic \- sufficient to contribute meaningfully to cross\-functional discussions without owning the engineering.
- Excellent communication skills; able to distill complex data into clear narratives for both technical teammates and non\-technical stakeholders.
- Comfortably operating with ambiguity and managing multiple workstreams simultaneously in a fast\-moving environment.
- Preferred:
- Experience with conversational analytics or NLP platforms (e.g., Qualtrics Conversational Analytics, Cognigy.AI, Google CCAI Insights, Gong, or similar).
- Familiarity with voice AI or IVR platforms (LiveKit, Sierra, Parloa, Replicant, or similar) from a measurement and QA perspective.
- Direct experience with LLM\-based products, prompt evaluation, or retrieval\-augmented generation (RAG) quality assessment.
- Background in fintech, financial services, or debt relief / credit counseling.
- Experience with A/B testing and experimentation frameworks applied to conversational flows.
- Formal UX research certification or training (Nielsen Norman Group, UXQB, or similar).
National Debt Relief Role Qualifications:* Computer competency and ability to work with a computer.
- Prioritize multiple tasks and projects simultaneously.
- Exceptional written and verbal communication skills.
- Punctuality expected, ready to report to work on a consistent basis.
- Attain and maintain high performance expectations on a monthly basis.
- Work in a fast\-paced, high\-volume setting.
- Use and navigate multiple computer systems with exceptional multi\-tasking skills.
- Remain calm and professional during difficult discussions.
- Take constructive feedback.
Compensation Information: Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for each position across the US. Within the range, individual pay is determined by work location, job\-related skills, experience, and relevant education or training. This good faith pay range is provided in compliance with NYC law and the laws of other jurisdictions that may require a salary range in job postings. The salary for this position is $114,500 \- $131,500 annually. About National Debt Relief:
National Debt Relief was founded in 2009 with the goal of helping an expanding number of consumers deal with overwhelming debt. We are one of the most\-trusted and best\-rated consumer debt relief providers in the United States. As a leading debt settlement organization, we have helped over 450,000 people settle over $10 billion of debt, while empowering them to lead a healthier financial lifestyle and feel free to live their best life. At National Debt Relief, we treat our clients like real people. Our purpose is to elevate, empower, and transform their lives.
Rated A\+ by the Better Business Bureau, our goal is to help individuals and families get out of debt with the least possible cost through conducting financial consultations, educating the consumer and recommending the appropriate solution. We become our clients' number one advocate to help them reestablish financial stability as quickly as possible.
Want to learn more about who we are? Connect with us on social!
Benefits:
National Debt Relief is a team\-oriented environment full of rewards and growth opportunities for our employees. We are dedicated to our employee's success and growth within the company, through our employee mentorship and leadership programs.
Our extensive benefits package includes:* Generous Medical, Dental, and Vision Benefits
- 401(k) with Company Match
- Paid Holidays, Volunteer Time Off, Sick Days, and Vacation
- 12 weeks Paid Parental Leave
- Pre\-tax Transit Benefits
- No\-Cost Life Insurance Benefits
- Voluntary Benefits Options
- ASPCA Pet Health Insurance Discount
- Wellness Incentive Program
National Debt Relief is a certified Great Place to Work®! *National Debt Relief is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other status protected by law.* *For information about our Employee Privacy Policy, please see* *here*
*For information about our Applicant Terms, please see* *here*
\#LI\-REMOTE
Salary Context
This $114K-$131K range is above 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 National Debt Relief, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($123K) sits 26% below the category median. Disclosed range: $114K to $131K.
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.
National Debt Relief AI Hiring
National Debt Relief has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $131K - $131K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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.
Frequently Asked Questions
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