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Job Summary:
Revance is building an Intelligence Platform to serve as the single source of truth for insights, recommendations, and knowledge across the company. We are seeking a Data & AI leader to lead this transformation.
You will own the full arc from data foundation to business reporting to actionable intelligence: ingesting data from dozens of sources, resolving entities into a connected intelligence layer, powering AI and ML models on top that drive measurable commercial outcomes across both practice-level (B2B) and consumer-level (B2C) touchpoints. You will create clarity of intelligence, ensuring every insight is trusted, explainable, and directly tied to growth.
- Reporting to: Chief Technology Officer
- Location: Nashville, TN or Dallas, TX or Remote
What You'll Do:
Execute the Intelligence Vision & Strategy
- Execute the enterprise intelligence strategy as the knowledge and insight hub across the Revance portfolio
- Design and operationalize a self-reinforcing intelligence system where better data produces better models, smarter actions, and improved outcomes
- Translate business goals into a sequenced intelligence roadmap with measurable topline impact
- Establish clarity of intelligence: unified data unified semantics trusted insights automated action revenue lift
Power Commercial Intelligence & Growth Engines
- Deliver AI-powered engines that can operate at scale
- Embed intelligence directly into commercial workflows (CRM, ordering, loyalty, digital platforms) so insights drive action at the point of decision
- Tie intelligence output to measurable outcomes: AOV, conversion, retention, share of wallet, pipeline velocity
Architect a Scalable Intelligence Platform
- Design a modern data architecture that connects data across many sources into a coherent, well-modeled intelligence layer
- Own the semantic layer and metrics governance: consistent business definitions, reusable governed metrics, and decoupled business logic enabling self-service analytics and AI consumption
- Ensure platform scalability, reliability, governance, cost efficiency, and explainability at every layer
Build & Lead the Intelligence Organization
- Define and build the AI & Data Engineering team and establish collaboration models with other stakeholders
- Lead and coordinate strategic vendors, ensuring all work aligns with long-term architecture, not just short-term deliverables; transition critical capabilities in-house over time
- Establish a culture of outcome-driven intelligence - every model, metric, and pipeline must trace to business impact
- Own data governance, quality, lineage, observability, and compliance across all data domains, including AI guardrails (bias checks, human-in-the-loop, auditability)
What You'll Bring:
Strategic & Leadership
- 10+ years in data, analytics, and AI, with at least 5+ years leading data or AI/ML teams
- Proven track record of building compounding intelligence systems that directly drove revenue growth, ideally in B2B, B2C, or B2B2C environments
- Experience translating data strategy into executive narratives and cross-functional alignment; ability to make complex concepts intuitive for non-technical leaders
- Track record of building and scaling high-performing teams (data engineering, data science, analytics) from early stage to mature function
Technical Architecture & Execution
- Platform Leadership & Intelligence — Led modernization of the enterprise data platform to create an AI-ready environment; championed integration of ML/LLM capabilities directly into the data core
- Unified Intelligence Architecture — Built a unified data layer that brings signals from many different source systems together into a single, trusted source of truth, creating the foundation that analytics and AI/ML models run on.
- Operational Excellence (DataOps/MLOps) — Established disciplined DataOps and MLOps practices, with automated, well-tested pipelines that deliver data reliably and keep models monitored and maintained once they are in production.
Domain & Commercial Intelligence
- Experience operationalizing AI enabled recommendation systems at scale with measurable commercial lift
- Background in healthcare, life sciences, retail, or regulated industries is a strong plus
- Understanding of loyalty ecosystems, CRM intelligence, and omnichannel personalization is a plus
Basic Qualifications:
- Doctorate degree & 5 years of directly related experience + OR
- Master's degree & 7 years of directly related experience + OR
- Bachelor's degree & 9 years of directly related experience
Company Summary:
Revance is a global company developing, producing, and distributing industry-leading, differentiated products across aesthetics, skincare, and therapeutics. Revance drives innovation beyond convention to offer treatment options for individuals across generations. The Company's vision is to redefine excellence in aesthetics, skincare, and therapeutics through science-powered innovation, with an unwavering commitment to its providers, patients, and consumers. Revance's award-winning products are the result of robust research and development, a cornerstone for the Company, driven by renowned scientists. For more information about Revance, please visit us at www.revance.com.
What Revance invests in you:
- Competitive Compensation including base salary and annual performance bonus.
- Flexible PTO, holidays, and parental leave.
- Generous healthcare benefits, HSA match, 401k match, employer paid life and disability insurance, pet insurance, wellness discounts and much more!
This section of the job description is required by the American with Disability Act (ADA). The ADA requires that job descriptions reflect the physical and mental demands required to effectively perform the essential duties of the job. The ADA prohibits employers from discriminating against a "qualified individual with a disability" in all aspects of the employment relationship. A "qualified individual with a disability" is "an individual who meets the education, experience, skill, and other job-related requirements of a position held or desired, and who, with reasonable accommodation, can perform the essential functions of a specified job."
Revance is an Equal Opportunity employer. All qualified applicants will receive consideration for employment based on merit, without regard to race, color, religion, sex, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Revance Therapeutics, 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 in Demand for This Role
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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Revance Therapeutics AI Hiring
Revance Therapeutics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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