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About This Role
As a Data Journalist specializing in AI and automation, you’ll work at the intersection of data, technology, research, and storytelling to help produce original, data-driven content for ConsumerAffairs. You’ll turn complex datasets into clear, compelling stories that are useful to consumers, earn media attention, and strengthen ConsumerAffairs’ authority.
This role is highly analytical and increasingly technical. In addition to developing story ideas and interpreting data, you’ll help us build smarter ways to research, analyze, QA, and produce data-driven content. That includes experimenting with AI tools and building and improving workflows to make repetitive work faster and more scalable.
You don’t need to be a software engineer, but you should be comfortable working with data beyond a basic spreadsheet. We’re looking for someone who understands quantitative analysis, can learn technical tools quickly, and is interested in using AI, automation, and code to solve editorial problems.
You might come from journalism, data journalism, statistics, economics, research, computer science, communications, or another related field. What matters most is your ability to work confidently with data, analyze it critically, identify the story behind the numbers, and continuously look for better ways to do the work.
Responsibilities Include:
Data Storytelling & Research
- Help develop original, data-driven story ideas that are timely, relevant, and useful to consumers.
- Research credible data sources and evaluate whether potential story ideas can be supported by reliable data.
- Help develop clear, defensible methodologies for analyzing data and measuring the topics we cover.
- Interpret datasets to identify meaningful trends, comparisons, outliers, and other findings that can shape the story.
- Think critically about what the data does — and does not — support, including limitations that should be considered when presenting findings.
- Identify opportunities to bring findings to life through charts, maps, tables, rankings, and other data visualizations.
Editorial Production & Project Management
- Turn research and data findings into clear editorial direction, including story angles, outlines, key findings, and guidance for freelance writers.
- Coordinate with data analysts and freelance writers throughout the production process, communicating expectations, answering questions, and keeping projects on track.
- Review and QA analysts’ work for accuracy, including source data, calculations, formulas, methodologies, and final results.
- Review writer drafts to ensure they follow the intended story structure and research direction and highlight the most important findings.
- Manage multiple projects at different stages of production while meeting deadlines and keeping stakeholders informed.
- Collaborate with editors, designers, Digital PR teammates, and other stakeholders to move projects from initial concept through publication.
AI & Automation
- Identify opportunities to use AI and automation to make research, data analysis, QA, and production workflows faster and more scalable.
- Build, test, and improve AI-assisted or automated workflows for repetitive production tasks while maintaining human review and editorial standards.
- Experiment with new tools and approaches that can improve the efficiency, accuracy, and consistency of data-driven projects.
- Document successful workflows and help integrate them into the team’s production process.
- Evaluate AI-generated outputs critically and independently validate data, calculations, sources, and findings before they are used in published work.
Creative & Strategic Thinking
- Stay curious about consumer trends, news, emerging datasets, and new approaches to data storytelling.
- Consider how research findings can be framed and presented to resonate with both consumers and journalists.
- Contribute ideas for improving our research, production processes, storytelling, and visual presentation.
- Use performance results and team feedback to understand what makes data-driven content successful and apply those lessons to future projects.
Requirements Minimum Qualifications & Credentials
- Bachelor’s degree in journalism, communications, data analytics, statistics, computer science, or a related field, or equivalent relevant experience.
- 1-3 years of experience in journalism, content, research, data storytelling, data visualization, digital PR, or a related area. Internships, academic projects, freelance work, and relevant coursework are welcome.
- Strong quantitative and statistical reasoning, including the ability to work confidently with percentages, rates, averages, distributions, rankings, changes over time, and other common forms of analysis.
- Strong writing and communication skills, with the ability to explain data and complex ideas clearly and accurately.
- Interest in researching and evaluating credible data sources, including government and other publicly available datasets; prior experience is a plus.
- Detail-oriented and committed to accuracy, with the ability to question results that seem unusual or inconsistent.
- Strong organizational skills and the ability to manage multiple projects, priorities, and deadlines.
Technical Skills
- Strong proficiency in Excel or Google Sheets, including formulas, filtering, sorting, lookups, conditional logic, pivot tables, and working with larger datasets.
- Working knowledge of basic statistical analysis and an understanding of how to choose appropriate ways to analyze and compare data.
- Ability to inspect, clean, transform, and validate datasets from multiple sources.
- Comfortable using APIs.
- Strong interest in building AI-assisted and automated workflows. Experience with tools such as ChatGPT, Claude, Gemini, Zapier, n8n, or similar platforms is a plus.
- Ability to use AI tools thoughtfully for research, coding, analysis, ideation, and production while independently validating outputs and maintaining editorial standards.
- Interest in data visualization; experience with Datawrapper, Flourish, Tableau, Infogram, or similar platforms is a plus.
- Familiarity with AP Style and digital publishing is a plus.
Benefits
Why You’ll Love Working Here At ConsumerAffairs, your voice matters. We foster a collaborative environment where you’re encouraged to take initiative, experiment boldly, and grow professionally. We're committed to work-life harmony, career development, and celebrating wins together.
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k)
- Life Insurance (Basic, Voluntary & AD&D)
- Paid Time Off (Vacation, Sick & Public Holidays)
- Family Leave (Maternity, Paternity)
- Short Term & Long Term Disability
- Training & Development
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At ConsumerAffairs, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
ConsumerAffairs AI Hiring
ConsumerAffairs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, Tulsa, OK, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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