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About This Role
From Aisle to Algorithm and for All Life’s Moments, at David’s Bridal, we empower our customers and our employees to stay true to their dreams and find the one, whether that means the event or the wedding dress that matches a personal style—or the career that is a perfect fit. Join a company that dominates the products in their category – 1 out of 3 being sold by them and taking care of them with one of the highest customer service scores in retail!
If you are passionately enthusiastic, endlessly curious, and customer obsessed, say “I do” and apply today!
About the Role
We are looking for a Data Analyst who doesn't just answer questions — they can't stop asking them. You're the kind of person who pulls a report, spots something slightly off in row 47, and four hours later has uncovered a trend nobody knew existed. You're technically sharp enough to build what you need and curious enough to keep digging until the story is clear.
This role sits at the intersection of rigorous analysis and modern AI. You will use LLMs, automation pipelines, and AI\-assisted tooling to dramatically accelerate insight generation — but the engine behind it all is genuine intellectual curiosity and the drive to understand why, not just what.
You will partner closely with product, marketing, operations, and leadership — not just to answer the questions on their roadmap, but to surface the ones they haven't thought to ask yet.
Built for analysts who are technically fluent and genuinely curious
The ideal candidate writes clean SQL and elegant Python — and also loses sleep over unexplained dips in conversion. You'll wield AI tools not because it's trendy, but because you want to spend more time on the interesting parts: the patterns, the hypotheses, and the so what.
Key Responsibilities
Core analysis \& insight generation
- Dig into data with genuine curiosity — follow threads, challenge assumptions, and don't stop at the surface\-level answer
- Design and execute investigations that surface non\-obvious insights, not just metric summaries
- Build and maintain SQL queries, Python scripts, and data models to support recurring and ad\-hoc analysis
- Own key performance dashboards and proactively flag when something looks interesting — or wrong
AI\-augmented workflows
- Use LLM APIs (e.g., Claude, GPT) to automate insight generation, narrative writing, and anomaly detection
- Build natural language interfaces that allow non\-technical stakeholders to query data without SQL
- Design and maintain AI\-powered reporting pipelines that deliver weekly/monthly commentary automatically
- Develop and test prompts for structured data extraction, classification, and summarization tasks
Stakeholder communication
- Turn complex findings into clear, compelling narratives — you make people care about the data, not just read it
- Come to meetings with a point of view, not just a chart; be willing to say what the data actually means
- Partner with business teams to frame the right questions — and push back when the wrong questions are being asked
Data infrastructure \& governance
- Collaborate with data engineering to define and document data models, transformations, and quality standards
- Contribute to the team's data catalog and ensure analytical assets are documented and reproducible
- Advocate for data quality, consistency, and governance across the organization
Skills \& Requirements
Required
- 3–6 years in a data analyst or analytics engineer role — you've seen enough data to know when something doesn't add up
- Advanced SQL — complex joins, window functions, CTEs; you write queries other analysts learn from
- Strong Python — pandas, numpy, visualization; comfortable moving from notebook to production\-ready script
- Demonstrated use of AI/LLM tools to accelerate analysis, automate reporting, or build smarter workflows
- A track record of finding insights that weren't in the original brief — proactive, not just reactive
- Strong written communication — you can write a two\-paragraph summary that makes a VP care about a cohort analysis
- Statistical fundamentals — hypothesis testing, regression, cohort analysis, knowing when correlation isn't causation
Preferred
- Experience with dbt, Airflow, or similar data transformation/orchestration tools
- Familiarity with cloud data warehouses — BigQuery, Snowflake, Redshift, or Databricks
- Hands\-on experience calling LLM APIs and building AI\-assisted workflows
- Exposure to machine learning — feature engineering, model evaluation, working with data scientists
- Experience with RAG pipelines, embeddings, or vector search for analytics use cases
Tech Stack
Python SQL dbt Airflow Tableau
Claude API OpenAI BigQuery Snowflake Looker
Streamlit Plotly pandas scikit\-learn Git
What We Offer
- Competitive salary commensurate with experience
- Flexible hybrid work arrangement — 2–3 days in office per week
- A culture that values curiosity — asking why is celebrated, not deflected
- Access to cutting\-edge AI tools and a team that actively encourages experimentation
- Dedicated budget for courses, conferences, and certifications
- Direct exposure to senior leadership and real ownership over insights that drive strategy
- Opportunity to shape the AI analytics approach of a growing organization
Now that we’ve popped the question, please say “I do”.
Full Time Opportunity – A comprehensive benefits package is available.
- Rewarding Environment and Competitive Pay
- Generous Dream Maker Discount After First Pay Period
- Referral Incentive Program
- Dayforce Wallet – Get Paid Early!
- Health/Dental/Vision Insurance
- 401K Program
- Paid Vacation, Wellness Days \& Holidays, including your Birthday off!
- Pet Benefits
Love wins when love is for Everyone!
Our mission at David’s Bridal is to embrace the ideas of Diversity, Equity, and Inclusion. It is our goal to build a workforce that is as representative as the customers we serve. We vow to create a culture where all forms of diversity are celebrated and seen as valuable.
David’s Bridal encourages applications from all qualified candidates. David’s Bridal has a great record of accommodating persons with disabilities. Contact Human Resources at [email protected] or 610\.943\.5048 if you need accommodation at any stage of the application process or want more information on our accommodation policies.
Policy: Candidate Use of AI in Live Interviews
We conduct interviews to evaluate each candidate’s own knowledge, judgment, and communication. During any live interview (virtual or in\-person), candidates must not use real\-time generative AI tools to compose or feed their answers. Candidates may use assistive technologies (e.g., screen readers, live captions) and may request reasonable accommodation in advance.
Disclaimer: The preceding job description has been designed to highlight the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive description of all duties, responsibilities and qualifications required of employees assigned to this job. Actual duties and responsibilities will vary. The standard base pay range for this role is posted at a minimum and maximum rate.
The starting rate of pay offered will vary based on factors including, but not limited to, position offered, location, training, and/or experience, and internal equity. This base pay range is specific to the state this role is posted in and may not be applicable to other locations. At David’s Bridal, it is rare for an individual to be hired at the high end of the range in their role, and compensation decisions are dependent upon the details and circumstances of each position and candidate.
Salary Context
This $74K-$112K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At David's Bridal, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($93K) sits 49% below the category median. Disclosed range: $74K to $112K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
David's Bridal AI Hiring
David's Bridal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $112K - $112K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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