Sr Advisor, Data Science

What to Expect

Join a global leader in the energy sector, where data science drives real-world impact across oil, power, renewables, and carbon markets. You’ll be embedded in a collaborative, entrepreneurial environment, working alongside experienced data scientists, software engineers, and commercial teams. Expect to tackle complex, high-impact problems, contribute to the energy transition, and see your work 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 (CI/CD, MLflow, monitoring) on 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 within the business 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

This position has a base salary range of $198,000 – $242,000.

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…