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About This Role
Job Title: Financial Analyst – AI Reporting & Dashboards (Claude)
Company: Paul Logistics
Location: Tulsa, OK (Onsite)
Job Type: Full-Time
Schedule: Monday-Friday 8a-5p
Salary: $60,000
Experience: 1-3 Years in financial analysis, FP&A, or accounting preferred
About the Role
Paul Logistics is seeking an AI-first Financial Analyst who uses Claude as their primary tool to analyze our financial and operating results and turn them into clear, decision-ready reports and dashboards. You do not need a traditional SQL or programming background. You do need a solid finance foundation and to be exceptionally skilled at using Claude to produce accurate, polished work quickly.
Reporting directly to the CFO, you’ll partner with leadership, accounting, pricing, and operations to support month-end reporting, budgeting, forecasting, and profitability analysis. You’ll use Claude to pull, clean, analyze, and present the numbers, owning each request from the first question to the finished report or dashboard.
Why Work With Us?
At Paul Logistics, we operate with a simple philosophy: backed by assets. built for service. Our team is growing, and we’re investing in the tools and talent needed to scale efficiently. This role offers direct exposure to leadership, meaningful ownership of projects, and the opportunity to shape how AI is used in finance and reporting across the company.
Key Responsibilities
- Prepare monthly financial reporting packages, including budget-vs-actual and variance analysis by entity, division, and department
- Support budgeting, forecasting, and cash flow projections, and update forecasts as results come in
- Analyze profitability by customer, lane, truck, and business unit, and flag trends, risks, and opportunities for leadership
- Build financial models and ad hoc analyses for leadership decisions such as equipment purchases, pricing, new locations, and acquisitions
- Build KPI dashboards and scorecards using Claude (interactive dashboards, Excel workbooks, and presentation-ready summaries)
- Turn exports from our TMS, accounting system, and spreadsheets into clean, structured datasets with Claude
- Verify every output: reconcile numbers back to the general ledger and source data, check formulas and totals, and catch errors before reports go out
- Build reusable Claude workflows (projects, skills, templates, and saved prompts) and automate recurring reports with scheduled tasks and connected apps (e.g., Microsoft 365, SharePoint, Excel)
- Support month-end close, account reconciliations, and audit requests as needed
Qualifications
- Bachelor’s degree in Finance, Accounting, Economics, or a related field
- Solid understanding of financial statements (P&L, balance sheet, cash flow) and basic accounting concepts
- Strong Excel and financial modeling skills (pivot tables, lookups, formulas, three-statement or driver-based models)
- Expert-level, daily, hands-on use of Claude; a portfolio of 2–3 analyses, reports, dashboards, or workflows you built with Claude is required
- Strong prompting skills: able to break a messy request into clear steps and iterate until the output is right
- Ability to present financial results clearly to non-financial leaders, in writing and in dashboards
- Extremely detail-oriented, with a habit of checking every AI output against the source rather than trusting it blindly
- Strong problem-solving mindset with the ability to work independently and manage multiple requests
- SQL or programming experience is not required
Preferred
- Experience in logistics, transportation, or supply chain finance
- Understanding of freight KPIs (revenue per mile, cost per mile, margin, utilization, deadhead)
- Familiarity with TMS platforms (e.g., McLeod) and accounting/ERP systems
- Experience with multi-entity financial reporting or consolidations
- Experience with Claude Code, Claude skills, or connecting Claude to business tools
- Exposure to Power BI, Tableau, or other BI tools
Benefits
- Health, dental, and vision insurance
- 401(k) with company match
- Paid Time Off + Paid Holidays
- Career Growth and Development Opportunities
Why Paul Logistics?
Paul Logistics is a growing, asset-based transportation and logistics company headquartered in Tulsa, Oklahoma. We combine owned assets with a strategic brokerage network to deliver reliable, high-quality service across the country. Our team is focused on execution, accountability, and continuous improvement; creating an environment where employees can grow and make a real impact.
Keywords: Financial Analyst, Data Analyst, Supply Chain, Transportation, Logistics, Freight Markets, Excel, Power BI, Market Strategy, Artificial Intelligence, AI Tulsa Jobs, Tulsa, OK, Oklahoma
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Paul Logistics, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Paul Logistics AI Hiring
Paul Logistics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tulsa, OK, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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