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About This Role
Data & AI Analyst
The Opportunity
- Data and AI Analyst at a growth-oriented energy company
- Collaborate with senior technical professionals to develop the data and AI foundations that support critical Refining & Logistics, Retail, and Finance operations across Par Pacific.
- Gain hands-on experience developing and deploying enterprise software solutions in Google Cloud Platform (GCP).
- Work closely with business and technical teams to understand complex business processes and transform manual, repetitive work into automated, scalable cloud-based workflows.
- Grow your skills through hands-on work by building real-world solutions as part of larger technology initiatives, with experienced technical professionals providing mentorship and guidance on architecture, standards, and code reviews.
- Reporting to the Director, Enterprise Data and Applications, you will work day-to-day with our Senior Developer and Data Architect, to contribute to the development of data pipelines, internal tooling, and AI-enabled automation.
Key activities include:
Data & SQL
- Assist in building and maintaining ETL workflows on Google Cloud Platform, primarily in BigQuery, with support from the Data Architect on modeling and design decisions
- Contribute to data quality checks, validation logic, and documentation for pipelines the team owns
- Support ad hoc data pulls and analysis requests from business teams, escalating larger requests to the appropriate data resource
Cloud & Development Practices
- Deploy and maintain assigned components in GCP under the direction of senior team members, building foundational fluency in the services Par Pacific already runs on
- Learn and apply the team's development practices: version control, code review, environment promotion, basic CI/CD, and infrastructure-as-code exposure
- Assist with monitoring, logging, and troubleshooting for workflows the team supports
- Follow established security, access, and governance standards; raise questions early when a requirement is unclear
AI & Automation
- Sit with business stakeholders to understand how work actually gets done today — the manual steps, the handoffs, the spreadsheets — and help document those processes clearly
- Partner with the Senior Developer and Data Architect to evaluate where AI and automation are a good fit, and where they are not
- Translate agreed-upon business logic into working prototypes, then help harden those prototypes into reliable, automated services deployed through the enterprise's existing cloud platforms
- Assist in integrating AI components with enterprise data sources and identity/access tooling, connecting the pieces between systems that do not yet talk to each other
- Contribute to testing, evaluation, and iteration of automated workflows once they are live
Collaboration
- Work closely with a small, lean technical team where your work is visible and your questions get answered directly
- Coordinate with business, ERP, and infrastructure teams to gather requirements and confirm assumptions before building
- Communicate progress, blockers, and findings clearly to both technical and non-technical audiences
Note: This description is intended to give you a general overview of the position. Additional responsibilities and opportunities may be identified based on current business needs.
Qualifications
To meet the basic qualifications for this role, you will have legal authorization to work permanently in the United States for any employer without requiring a visa transfer or visa sponsorship.
Additional qualifications include:
- High School Diploma or equivalent required; Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related field preferred
- Minimum 1 year of professional experience in software development, data engineering, data analysis, or a comparable technical role required; relevant internship, co-op, bootcamp, or substantial personal project work will be considered
- Working proficiency in SQL, including joins, aggregation, and window functions, with the ability to reason about query performance, required
- Proficiency in Python for scripting, data manipulation, and automation preferred
- Foundational understanding of cloud concepts and at least introductory hands-on exposure to GCP or AWS (compute, storage, IAM basics) required
- Familiarity with modern AI tooling — LLM APIs, prompting, or agent frameworks — and a practical sense of what these tools are and are not good at, required
- Demonstrated ability to break a problem down, ask clarifying questions, and communicate a solution in plain language required
- Comfort working in a lean environment where priorities shift
- Experience with Git and collaborative version control workflows preferred
- Exposure to data modeling concepts (dimensional, relational, or semantic) preferred
- Exposure to BI tooling such as Looker or Power BI preferred
- Exposure to containers, CI/CD pipelines, or infrastructure-as-code preferred
- Exposure to workflow orchestration tools such as Apache Airflow or Cloud Composer preferred
- Familiarity with oil & gas, retail, or finance data types (e.g., retail data, SAP extracts, operations feeds) preferred
- Cloud fundamentals certification (GCP Associate Cloud Engineer, AWS Cloud Practitioner, or equivalent) preferred
About Us
At Par Pacific, we own and operate market-leading energy and infrastructure businesses in logistically complex markets. We have built a team of downstream industry veterans and subject matter experts to lead our organization with an entrepreneurial spirit and a collaborative, problem solving approach. We bring our diverse strengths and motivation to complex markets where we seek out diamonds in the rough. As a nimble, growing organization, we actively pursue new opportunities with corporate financing know-how, respond to local market demands, and adapt to changing external environments.
More Good Reasons to Work for Par Pacific
Unique Culture and RICH Values
There is nothing ordinary about Par Pacific - a niche player in the energy space. And there's certainly nothing ordinary about Par Pacific employees. We are driven, hardworking, entrepreneurial-minded professionals who love to win. Our RICH values describe how we approach our work: Respect for Others, Integrity, Collaborative Innovation, and Heart. If that describes you, you'll fit right in.
Location, Location, Location
Because Par Pacific operates in some of the most beautiful places throughout the country, our employees get to make these places their home and enrich their lives. The office where you'll work is in the CityCentre in Houston, Texas, located on the west side of Houston near I-10 and the Sam Houston Tollway. It's a vibrant area of the city with plenty of places for lunch or to wind down after work.
Opportunistic Growth Strategy
At Par Pacific, we look for operations with strong fundamentals and great employees who can move a business forward. Our management team has deep experience in the energy industry, as well as in leading mergers, acquisitions, and integrations of newly acquired companies.
Focused on the Future
We are an energy company that has recognized the imperative and embraced the opportunity to begin the transition from fossil fuels. We are developing products that help reduce greenhouse gas emissions.
Excellent Compensation and Benefits
In addition to a competitive salary, we offer a strong comprehensive benefits package that includes medical, dental and vision insurance, a robust allotment of paid time off, a 401(k) with company match, retirement savings plans, educational reimbursement, and more. Full-time employees (and their families) are offered medical (with prescription coverage), dental, vision, voluntary critical illness, accident, life/AD&D insurance, Flexible Spending Account and Employee Assistance Program. Employees can purchase company stock at discounted prices through the Employee Stock Purchase Program.
Our Headquarters
Par Pacific Holdings, Inc., based in Houston, Texas, owns, manages and maintains interests in energy and infrastructure businesses. We are a growing energy company focused on renewable and conventional fuels in the Western United States. Par Pacific also owns 46% of Laramie Energy, LLC, a natural gas production company with operations and assets concentrated in Western Colorado. Our common stock is publicly traded on the NYSE under the trading symbol "PARR".
Our Refineries and Logistics Operations
We own and operate refineries in Hawaii, Montana, Washington, and Wyoming, with total operating throughput capacity of over 219 Mbpd. Par Pacific owns and operates one of the largest energy networks in Hawaii with 94,000 bpd of operating refining capacity, a logistics system supplying the major islands of the state and 90 retail locations. In the Pacific Northwest and the Rockies, Par Pacific owns and operates 125,000 bpd of combined refining capacity and related multimodal logistics systems.
Our Retail Operations
We own and operate over 90 retail locations in Hawaii under the Hele and 76 brands and over 30 retail locations in Washington and Idaho under the nomnom brand.
Par Pacific Is an Equal Opportunity Employer
Par Pacific prohibits discrimination and harassment of any type and affords equal employment opportunities to employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, genetic information or any other status protected by applicable law.
Note: Par Pacific will not accept calls or unsolicited resumes from third-party recruiters. All candidates are required to apply through this web posting.
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 Par Pacific Holdings, 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.
Par Pacific Holdings AI Hiring
Par Pacific Holdings 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, 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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