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About This Role
ESSENTIAL DUTIES AND RESPONSIBILITIES
The Senior Manager will own the roadmap for Pricing & Revenue Science and lead the development and application of models that explain and forecast demand, conversion, price sensitivity, utilization, and revenue performance. Responsibilities include designing pricing and business experiments; building demand and revenue forecasts; developing optimization approaches that balance conversion, margin, and capacity; and creating analytical frameworks that quantify the impact of geography, mileage, seasonality, inventory availability, customer segment, competitive conditions, and other business drivers. This leader will establish standards for causal measurement, model validation, data quality, reproducibility, monitoring, and post-deployment performance. The role partners closely with Product, Engineering, IT, Finance, Operations, Marketing, and other business teams to translate analytical work into scalable decision tools and operating processes. The Senior Manager will also lead root-cause analysis when performance shifts, communicate recommendations to senior leaders, and coach a high-performing team of data scientists and analytical professionals.
MANAGEMENT & SUPERVISORY RESPONSIBILTIES
- Reports to Vice President, Digital
- Directly manages the Pricing & Revenue Science Team, including Data Scientists and pricing/revenue analytics professionals. Full management authority including hiring, performance management, compensation decisions, and terminations. Responsible for team priorities, analytical standards, and vendor/partner oversight as applicable.
JOB QUALIFICATIONS: Essential Skills, Abilities and Example Behavior(s)
- Deep experience leading applied data science or decision science work in pricing, revenue management, forecasting, optimization, marketplace economics, or a related analytical domain
- Strong hands-on fluency in SQL and Python; able to review complex queries, notebooks, model code, and analytical pipelines. Experience with R or another statistical language is a plus
- Strong command of statistics and applied econometrics, including regression, hypothesis testing, causal inference, experimental design, measurement, and model validation
- Experience with predictive modeling, machine learning, forecasting, elasticity modeling, and optimization; able to evaluate methodology, feature selection, validation approaches, explainability, and business applicability
- Familiarity with modern data science tooling and environments, including the Python analytics ecosystem (e.g., pandas, NumPy, scikit-learn, statsmodels or equivalents), cloud data warehouse/lake platforms, and Git/version control
- Ability to partner with Engineering and IT to productionize analytical models and decision systems, with working knowledge of data pipelines, APIs, model monitoring, drift, data quality, and deployment lifecycle practices
- Ability to translate complex technical findings into clear business recommendations and influence senior leaders and cross-functional stakeholders using data-driven insights
- Proven track record leading high-impact analytical initiatives from discovery through implementation and developing data scientists and analysts with strong standards for rigor, documentation, and reproducibility
JOB QUALIFICATIONS: Education & Experience Requirements
- Bachelor's degree required in Data Science, Statistics, Computer Science, Economics, Applied Mathematics, Operations Research, Engineering, or a related quantitative field; Master's degree or PhD preferred but not required
- 8+ years of experience in data science, pricing analytics, revenue management, forecasting, optimization, or advanced analytics, with at least 3 years leading or managing data scientists or analytical professionals
- Experience applying predictive, experimental, forecasting, or optimization methods to high-volume commercial or operational decisions; experience in e-commerce, marketplaces, logistics, services, pricing, or revenue management preferred
PHYSICAL REQUIREMENTS
- Ability to sit at a desk and use a computer for extended periods; vision sufficient to view details on a computer monitor
- Ability to stand and walk up to 8 hours a day; ability to stoop, bend and lift boxes weighing up to 50 lbs.
- Ability to hear and verbally communicate using a telephone handset and/or connected headset device
WORKING CONDITIONS
- Regular business hours. Some additional hours may be required.
- Travel requirements: Negligible
- Climate-controlled office environment during normal business hours.
- Regular attendance and punctuality required
- May be subject to pre-employment criminal background check and/or drug screening as well as random drug screenings in accordance with company policy
DISCLAIMER
The preceding job description has been designed to indicate the general nature of work performed; the level of knowledge and skills typically required; and usual working conditions of this position. It is not designed to contain, or be interpreted as, a comprehensive listing of all requirements or responsibilities that may be required by employees in this job.
Equal Opportunity, Affirmative Action Employer
PODS Enterprises, LLC is an Equal Opportunity, Affirmative Action Employer. We will not discriminate unlawfully against qualified applicants or employees with respect to any term or condition of employment based on race, color, national origin, ancestry, sex, sexual orientation, age, religion, physical or mental disability, marital status, place of birth, military service status, or other basis protected by law.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 PODS, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
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.
PODS AI Hiring
PODS has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Clearwater, FL, US, Clearwater, FL.
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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