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About This Role
Phillips 66 & YOU - Together we can fuel the future
The Sr. Advisor III, Commercial Data Scientist serves as a high-autonomy technical subject matter expert for advanced analytics and machine learning across the Commercial organization. Operating as the senior technical partner to the Chief Data Scientist, the Commercial Data Scientist will independently own desk relationships, deliver high value work, and set the standard for the team as the founding hire for commercial data scientist capability embedded with the trading desks. The Commercial Data Scientist will partner directly with traders and market analysts, turn messy market and operational data into decision-quality signals, and establish the validated data products and production workflows that let the broader team scale.
The desks have concrete opportunities in ship tracking, supply and demand modeling, outage analytics, arbitrage, and scenario analysis, plus a cross-desk need to stop rebuilding the same work. Because no model is trustworthy without robust upstream data, this hire will both deliver high-value trading use cases and help build the data science operating model on governed, business-ready data.
What You'll Do
Partner directly with traders, strategists, market analysts, and Commercial leaders to frame high-value problems, identify opportunities, and integrate validated analytical insights into business and trading decisions.
Lead end-to-end data science initiatives across forecasting, scenario analysis, optimization, anomaly detection, signal generation, and other advanced analytics use cases.
Identify, assess, and enhance internal, external, and alternative data sources for quality, coverage, timeliness, and commercial relevance; establish trusted, business-ready data products.
Translate desk-specific analyses and solutions into reusable methods, modeling patterns, and tools that can scale across commodities, markets, and Commercial teams.
Develop production-quality code and partner with data engineering, ML engineering, and software development teams on deployment, monitoring, CI/CD, MLflow, and durable MLOps workflows.
Set technical direction and reusable foundations for the team, participate in tooling and architecture decisions, and mentor data scientists and other technical team members without relying on formal management authority.
Communicate complex technical concepts, model results, uncertainty, and recommendations clearly to technical peers, commercial stakeholders, and senior leadership; develop or integrate analytical solutions into dashboards and existing platforms to improve decision speed.
What You'll Bring – Required
Legally authorized to work in the United States
Master’s degree or higher in Computer Science, Data Science, Machine Learning, Statistics, Applied Mathematics, Economics, Engineering, Physics, or a related quantitative field
6 or more years of industry experience developing and deploying advanced analytics and machine learning solutions, including a track record of independently owning ambiguous problems from hypothesis through business adoption
Strong Python data science skills and production-quality coding experience
Strong mathematical and statistical foundations, including inference, time-series modeling, econometrics, optimization, and rigorous model evaluation
Demonstrated ability to extract insights from large, complex, multi-source datasets and translate analytical findings into actionable commercial recommendations
Excellent communication and stakeholder management skills, with the ability to influence technical peers, commercial partners, and senior leaders
What Makes You Stand Out – Preferred
PhD in Computer Science, Data Science, Machine Learning, Statistics, Applied Mathematics, Economics, Engineering, Physics, or a related quantitative field
Experience in energy, commodities, trading, financial markets, or another decision-intensive commercial environment, with an understanding of price formation, market structure, and risk
Depth in time-series analysis, forecasting, optimization, geospatial data, anomaly detection, or applied machine learning used in live business decisions
Experience shaping shared research, modeling, or data-product infrastructure rather than delivering only one-off analyses
Experience working across front office, risk, compliance, IT, Digital, and other cross-functional teams to deliver durable business outcomes
Compensation Range
This position has a base salary range of $180,900 - $221,100. This role is eligible to participate in the company’s commercial bonus program, offering the opportunity to earn additional compensation based on business and individual performance, in accordance with program guidelines.
At Phillips 66, we are committed to pay transparency and competitive, equitable compensation. Each role is assigned a salary grade with a defined pay range, benchmarked against industry peers. Where a candidate offer falls within the posted range depends on the candidate's experience, skills, and alignment with the role’s requirements. Offers are made to ensure internal equity and market competitiveness. Our compensation programs are designed to reward performance and support career growth.
The Commercial organization works to effectively leverage assets and market knowledge to create additional value within the risk parameters of the Company. We do this by maximizing general interest profitability, enhancing return on capital employed by successfully partnering with the Refining, Transportation and Marketing functions to ensure Value Chain Integration. Our truck and rail fleets support our feedstock and distribution operations. Rail movements are provided via a fleet of more than 10,000 owned and leased rail cars. Truck movements are provided through numerous third-party trucking companies, as well as through our 100 percent-owned subsidiary, Sentinel Transportation LLC.
Total Rewards
At Phillips 66, providing access to high quality programs and care for you and your family is important to us. Maintaining a culture of well-being — physical, emotional, social, and financial — is essential for a high-performing organization. When we are at our best, we are poised to deliver exceptional results — personally and professionally. Benefits for certain eligible, full-time employees include:
Annual Variable Cash Incentive Program (VCIP) bonus
8% 401k company match
Cash Balance Account pension
Medical, Dental, and Vision benefits with an annual company contribution to a Health Savings Account for employees on HDHP
Total well-being programs and incentives, including Employee Assistance Plan, well-being reimbursement, and backup family care services
Learn more about Phillips 66 Total Rewards (http://hr.phillips66.com) .
Phillips 66 has more than 140 years of experience in providing the energy that enables people to dream bigger and go farther, faster. We are committed to improving lives, and that is our promise to our employees and our communities. We are sustained by the backgrounds and experiences of our diverse teams, which reflect who we are, the environment we create and how we work together. We have been recognized by the Human Rights Campaign, U.S. Department of Labor and the Military Times for our continued commitment to inclusive practices and policies in the hiring and retention of those in the LGBTQ+ community and military veterans. Our company is built on values of safety, honor and commitment. We call our cultural mindset Our Energy in Action, which we define through four simple, intuitive behaviors: We work for the greater good, cultivate an environment of trust, seek different perspectives and pursue excellence.
Learn more about Phillips 66 and how we are working to meet the world's energy needs today and tomorrow, by visiting phillips66.com.
To be considered:
In order to be considered for this position you must complete the entire application process, which includes answering all prescreening questions and providing your eSignature on or before the requisition closing date of October 13, 2026 .
Candidates for regular U.S. positions must be a U.S. citizen or national, or an alien admitted as permanent resident, refugee, asylee or temporary resident under 8 U.S.C. 1160(a) or 1255(a)(1). Individuals with temporary visas such as E, F-1, H-1, H-2, L, B, J, or TN or who need sponsorship for work authorization now or in the future, are not eligible for hire.
Phillips 66 is an Equal Opportunity Employer
Salary Context
This $180K-$221K range is above the median for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Phillips 66, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($201K) sits 6% above the category median. Disclosed range: $180K to $221K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Phillips 66 AI Hiring
Phillips 66 has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Houston, TX, US. Compensation range: $152K - $221K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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