Sr Advisor, Data Science

Position Overview

Sr Advisor, Data Science at Phillips 66

Join a global leader in the energy sector where data science drives real-world impact across oil, power, renewables, and carbon markets. Embedded in a collaborative environment, you’ll tackle complex, high‑impact problems, contribute to the energy transition, and influence business decisions at scale.

• Analyze large, complex datasets to uncover insights, patterns, and trends that drive business strategy and operational improvements.
• Partner with trading desks, commercial teams, and IT/ML engineers to implement predictive analytics projects and deploy models using MLOps best practices on a Databricks‑on‑Azure tech stack.
• Design, build, and integrate data science solutions into existing systems and platforms for seamless user experiences.
• Perform complex statistical analysis, data mining, and visualization to enable data‑driven decision‑making.
• Contribute to reporting strategies, translating analytical findings into actionable recommendations for stakeholders.
• Share knowledge and build data science expertise through mentoring and collaboration.
• Actively participate in code reviews, experiment design, and tooling decisions to drive team quality and velocity.

• Master’s degree (or equivalent) in Computer Science, Data Science, Machine Learning, or a related field;
  Ph.D. is a plus.
• 3+ years of industry experience developing and deploying machine learning models and advanced analytics solutions.
• Proficiency in Python and ML frameworks (PyTorch, Tensor Flow, scikit‑learn); experience with Databricks, Spark, and Azure cloud services; familiarity with containerization (Docker) is a plus.
• Expertise in exploring and extracting insights from large multi‑source data sets.
• Strong problem‑solving skills and ability to work independently and collaboratively.
• Strong foundations in statistics, time series modeling, and econometrics.
• Excellent communication skills, able to explain complex technical concepts to non‑technical stakeholders.
• Advanced coursework in math, statistics, and machine learning.
• Demonstrable attention to detail and commitment to quality.
• Legally authorized to work in the posting country.

• Thrives in ambiguous, high‑volume data environments, accurately defining key elements and encouraging innovative analysis.
• Creates new and better ways for the organization to succeed, offering original ideas and enhancing others’ creative solutions.
• See’s ahead to future possibilities, translating trends and insights into breakthrough strategies for the business.
• Experience in the energy or commodities trading industry, with knowledge of financial markets and trading concepts.
• Proven ability to integrate machine learning systems into interactive dashboards (e.g., Dash, Streamlit) and present use cases to non‑technical colleagues.
• Resourceful, adaptable, and motivated to make an impact in a dynamic, fast‑growing team.

Base salary range: $198,000 – $242,000. The total rewards package includes a competitive variable incentive program and benefits that support career growth.

• Annual Variable Cash Incentive Program (VCIP) bonus.
• 8% 401(k) 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 and family care services.

To be considered, complete the entire application process—including answering all prescreening questions and providing your eSignature—on or before the requisition closing date of 01/18/2026
.

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.