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About This Role
Role Summary:
URBN is seeking a Data Scientist to develop AI\-powered visual experiences, with a primary focus on image and video generation. This individual will lead algorithm development and Image/Video Generative AI initiatives, leveraging generative AI models, multimodal data sources, and modern creative AI workflows to drive innovation across our digital ecosystem. The role involves close collaboration with leadership, Product Management, Creative, and Engineering teams.
As Data Scientist, you will play a pivotal role in building and orchestrating image and video generation pipelines that power AI\-first innovations at URBN, from product imagery and creative content to virtual try\-on and beyond. You will drive the development, implementation, and validation of generative visual AI solutions across a range of applications to support our product lifecycle.
This role blends deep technical fluency in generative models with a strong visual sensibility. The ideal candidate combines a solid machine learning foundation with hands\-on experience in the rapidly evolving image and video generation landscape, including diffusion models, multimodal architectures, and GenAI workflow orchestration tools. You should be able to evaluate generated outputs not just quantitatively, but with a critical creative eye. If you are passionate about the intersection of generative AI and visual storytelling, and you stay current with the fast\-moving landscape of image and video models, we invite you to help shape the future of intelligent, AI\-native creative experiences at URBN.
Role Responsibilities:
- Design, implement, and optimize image and video generation pipelines using state\-of\-the\-art models to produce high\-quality visual content at scale.
- Build and maintain multi\-model generative workflows using orchestration tools that chain together generation, inpainting, upscaling, style transfer, and conditioning steps into production\-ready pipelines.
- Fine\-tune and adapt image generation models using techniques such as LoRA, DreamBooth, ControlNet, IP\-Adapter, and textual inversion to achieve brand\-consistent, style\-controlled outputs.
- Leverage multimodal and vision\-language models for image understanding, visual analysis, automated tagging, and quality evaluation within generative workflows.
- Evaluate, prototype, and integrate emerging video generation models into creative and product workflows.
- Develop agentic AI pipelines that orchestrate multi\-step visual content creation, from prompt generation and image synthesis to post\-processing and delivery.
- Collaborate with cross\-functional teams including Creative, Product Management, and Engineering to translate brand and business needs into scalable generative AI solutions.
- Lead the technical evaluation of new generative AI models, tools, and vendors as the landscape evolves, influencing decisions for URBN's visual AI technology stack.
- Guide data curation and preparation strategies for fine\-tuning, including dataset construction, annotation workflows, and synthetic data generation.
- Analyze and benchmark model outputs for quality, consistency, and brand alignment, designing robust validation and feedback loops that combine quantitative metrics with qualitative human assessment.
- Partner with engineers to translate research prototypes into production\-grade services and APIs, with attention to cost optimization and throughput at scale.
Role Qualifications:
- 1\+ years of industry experience in data science, machine learning, or AI engineering, with a strong foundation in ML fundamentals.
- 1\+ year of hands\-on experience working with image generation models in a professional or serious applied context, not casual experimentation.
- Strong proficiency in Python, with practical experience using ML frameworks such as PyTorch and Hugging Face multimodal models.
- Hands\-on experience with image model fine\-tuning and conditioning techniques
- Working knowledge of GenAI workflow orchestration tools for building multi\-step generation pipelines.
- Experience with multimodal and vision\-language models for image understanding, captioning, or visual analysis.
- Experience with cloud\-based AI infrastructure for training, fine\-tuning, and serving generative models.
- Proven ability to evaluate and rapidly adopt new generative AI models and tools as the field evolves.
- Strong visual sensibility, with an eye for image quality, composition, and brand consistency in generated outputs.
- Excellent communication and collaboration skills, with the ability to bridge technical and creative teams.
- Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Engineering, or Mathematics, or equivalent practical experience.
\#LI\-MW3
The Perks:
URBN offers comprehensive Perks \& Benefits to employees. Availability and eligibility to specific benefits may be subject to your location and employment status. Benefits include medical, dental, vision, PTO, generous employee discounts, retirement savings and much more! For additional information visit www.urbn.com/work\-with\-us/benefits
EEO Statement:
URBN celebrates diversity and is committed to creating an inclusive environment for all employees. We are proud to provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, sex (including gender, pregnancy, sexual orientation, and gender identity or expression), religion, creed, age, physical or mental disability, national origin or ancestry, ethnicity, citizenship, service in the uniformed services, genetic information, or any other protected characteristic as established by law. We believe strongly in fostering a safe, fair and respectful work environment. To ensure compliance with our non\-discrimination and anti\-harassment policies, we offer anti\-harassment training to managers and employees.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At URBN, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 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.
URBN AI Hiring
URBN has 1 open AI role right now. They're hiring across Data Scientist. Based in Philadelphia, PA, 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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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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