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About This Role
Empower Pharmacy is a visionary healthcare company dedicated to making quality, affordable medication accessible to millions of patients nationwide. As the nation's most advanced 503A compounding pharmacy and FDA\-registered 503B outsourcing facility, we're redefining what's possible in personalized medicine and pharmaceutical manufacturing. We're proud to be recognized as one of Houston's fastest\-growing private companies and ranked \#116 in Healthcare \& Medical on the Inc. 5000 List for 2025\.
Our strength is built on four core values: People, Quality, Service, and Innovation. Guided by these principles, we've created a uniquely integrated healthcare platform powered by advanced technology, operational excellence, and a relentless commitment to patient care. From manufacturing and quality control to distribution and customer experience, our teams work together to raise industry standards, expand access to critical medications, and improve outcomes for patients and providers across the country.
At Empower, joining our team means more than starting a new role. It means becoming part of a mission\-driven organization that's transforming healthcare at scale. We invest deeply in our people, encourage bold thinking, and create opportunities for growth, leadership, and innovation at every level. Your ideas matter here, your development is supported, and the work you do has a direct impact on the lives of millions.
If you thrive in a fast\-moving, purpose\-driven environment where innovation, collaboration, and ambition come together, Empower Pharmacy is the place for you. Let's transform healthcare together.
Position Summary:
The Principal, Marketing Data Science builds the algorithmic and machine learning foundation that turns Empower's marketing data into predictive, self\-improving systems for growth. This role owns the design and development of attribution models, AI\-driven learning systems, and the algorithmic infrastructure that processes and activates marketing data at scale, directly supporting patient access, provider adoption, and long\-term value creation in a highly regulated healthcare environment. Traditional analytics and reporting remain part of the job, but the core of the role is hands\-on model building: developing the systems and algorithms that let Empower's marketing decisions get measurably smarter over time, not just measured after the fact
Responsibilities:
Model Development
- Model Building: Designs, builds, and continuously refines attribution and incrementality models as hands\-on technical work, treating model architecture, validation, and retraining as core ongoing responsibilities rather than something delegated or outsourced.
- AI Systems: Develops and deploys AI and machine learning systems, such as propensity models, predictive forecasting, and automated media optimization logic, that learn continuously from marketing and engagement data to drive decisions rather than just describe past performance.
- Model Testing: Conducts rigorous testing, validation, and performance monitoring of deployed models to ensure accuracy, stability, and continued predictive value as marketing conditions evolve.
Data \& Infrastructure
- Pipeline Architecture: Architects the algorithmic pipelines and data infrastructure that ingest, process, and operationalize marketing data at scale, partnering with marketing technology and IT to build systems and automated workflows, not just dashboards and reports.
- Systems Integration: Partners with marketing technology and IT teams to integrate new data sources and algorithmic outputs into existing platforms, ensuring scalable, production\-ready deployment.
- Data Quality: Establishes data quality standards and monitoring processes to ensure marketing data feeding models remains accurate, complete, and reliable over time.
Stakeholder Communication
- Executive Guidance: Translates model logic, assumptions, and outputs into clear, executive\-level guidance, ensuring leadership understands not just what a model concludes but how and why it works, while maintaining the traditional analytics and reporting this leader is also responsible for.
- Performance Reporting: Maintains ongoing analytics and reporting deliverables that track marketing performance, model impact, and business outcomes for stakeholders across the organization.
- Cross\-Functional Alignment: Collaborates with marketing, sales, and product teams to ensure model outputs are understood, adopted, and applied effectively in day\-to\-day decision\-making.
Governance \& Compliance
- Technical Authority: Serves as the technical authority on marketing data science, aligning model development with product, sales, operations, and compliance priorities, and ensuring all algorithmic and AI work meets HIPAA and healthcare data privacy requirements.
- Compliance Oversight: Reviews algorithmic and AI workflows on an ongoing basis to confirm continued alignment with HIPAA and healthcare data privacy standards as models and data sources evolve.
- Industry Awareness: Stays current on emerging machine learning techniques, marketing attribution methodologies, and regulatory developments relevant to healthcare marketing analytics.
Knowledge and Skills:
- Hands\-on fluency in Python or R and SQL, with experience building, validating, and productionizing predictive or causal models rather than relying solely on BI or visualization tools.
- Working knowledge of marketing attribution methodologies, causal inference and incrementality techniques, and applied machine learning (regression, classification, time series, propensity modeling), with the strategic thinking and communication skills to translate that work for senior stakeholders and ensure compliance with healthcare data privacy requirements.
- Strong understanding of statistical modeling techniques and experimental design principles used to validate causal and predictive relationships in marketing data.
- Ability to communicate complex technical concepts clearly to non\-technical stakeholders and translate analytical findings into actionable business recommendations.
Experience and Qualifications:
- A minimum of 5 years of experience building attribution models, machine learning systems, or algorithmic data products within complex, multi\-channel marketing environments, with direct hands\-on model development experience.
- Requires a bachelor's degree or equivalent work experience in data science, statistics, computer science, applied mathematics, or a related quantitative field; an advanced degree in a quantitative discipline is a plus.
- Proven track record of building and deploying models into production marketing environments, with measurable impact on campaign performance or business outcomes.
- Experience working within regulated industries, such as healthcare or financial services, where data privacy and compliance requirements shape technical decision\-making is preferred.
Key Competencies:
- Customer Focus: Builds trust through customer\-centric solutions.
- Strategic AI: Guides responsible AI adoption and adaptation.
- Optimizes Work Processes: Drives efficiency with continuous improvement.
- Collaborates: Partners effectively to achieve shared goals.
- Resourcefulness: Secures and deploys resources efficiently.
- Manages Complexity: Simplifies and solves complex challenges.
- Ensures Accountability: Delivers on commitments with integrity.
- Situational Adaptability: Adjusts approach to shifting conditions.
- Communicates Effectively: Tailors messages to diverse audiences.
Values:
- People: Empowering people defines who we are.
- Quality: Excellence in every product, every time.
- Service: Serving others is our highest purpose.
- Innovation: Advancing care through technology and discovery.
Employee Benefits, Health and Wellness:
We offer comprehensive benefits to support your health, well\-being, and future, including medical, dental, and vision coverage, paid time off, 401(k) matching, wellness perks, IV therapy, and compounded medications. Learn more: https://careers.empowerpharmacy.com/benefits/
Physical Requirements:
While performing the responsibilities of the job, the employee is required to talk and hear. The employee is often required to remain in a stationary position for a significant amount of the workday and frequently use their hands and fingers to handle or feel in order to access, input, and retrieve information from the computer and other office productivity devices. Employees are regularly required to move about the office and around the corporate campus. The employee is regularly required to stand, walk, reach with arms and hands, climb or balance, and to stoop, kneel, crouch or crawl.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Empower Pharmacy, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Empower Pharmacy AI Hiring
Empower Pharmacy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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