Staff Data Scientist

Global Partners LP•Newton, MA

1d•$160,500 - $240,800

About The Position

Global Partners is hiring a Staff Data Scientist to be the senior technical individual contributor for pricing and demand science and applied AI in the Central Data and Analytics Organization. Pricing decisions are made every day across our fuel and convenience store business, and this role builds the models, the experiments, and the AI systems that inform them. The core of the role is demand and price response modeling at scale, delivered as probabilistic forecasts that pricing teams and downstream applications use. You will own the modeling approach, the evaluation that decides whether a model is ready for production, and the path from forecasting toward automated pricing decisions. Measurement is the other half of the role. You will design and analyze pricing field experiments that establish how demand responds to price, and present the results and their limits to leadership and business stakeholders. You will also raise the technical standard of the team: defining how AI and agentic systems are evaluated, setting orchestration and observability practices for production workloads, reviewing designs on work delivered by others, and mentoring other data scientists. At Global Partners, business starts with with people. Since 1933, we’ve believed in taking care of our customers, our guests, our communities, and each other—and that belief continues to guide us. The Global Spirit is how we work to fuel that long term commitment to success. As a Fortune 500 company with 90+ years of experience, we’re proud to fuel communities—responsibly and sustainably. We show up every day with grit, passion, and purpose—anticipating needs, building lasting relationships, and creating shared value.

Requirements

Bachelor's degree in Computer Science, Statistics, Mathematics, Physics, or related quantitative field required
7+ years of industry data science or machine learning experience, including ownership of production models.
Deep expertise in demand forecasting and price response modeling, including probabilistic output and calibration.
Experience designing and analyzing field experiments, including power analysis and randomization.
Experience building and deploying agentic and large language model automation.
Experience evaluating AI systems using harnesses and benchmarks.
Expert level Python and SQL, with Git, code review, automated testing, and continuous integration.
Experience with a cloud machine learning platform, AWS SageMaker preferred, including model registry, monitoring, and alerting.
Experience with a cloud data warehouse, Snowflake preferred, and with transformation and orchestration tooling such as dbt and Dagster.
Experience delivering results through BI and application tooling such as Tableau and Streamlit.
Experience raising technical quality through specification, design review, and mentoring.
Strong written and verbal communication with both technical and business audiences.

Nice To Haves

PhD/Master's degree in Machine Learning, AI, or related discipline preferred.
Reinforcement learning experience a bonus.
Domain experience in fuel, retail, or another pricing intensive industry preferred.

Responsibilities

Own demand and price response modeling end to end, from problem framing through production.
Deliver probabilistic forecasts at daily and intraday resolution for pricing and planning use.
Compare candidate modeling approaches against strong baselines and select on evidence.
Define model evaluation and the criteria for promoting a model into production.
Advance pricing from forecasting toward automated price recommendation, with appropriate safeguards.
Design and analyze pricing field experiments, including power analysis and site selection.
Build and deploy agentic and large language model automation for analytical workflows.
Define how AI and agentic systems are evaluated, including harnesses, benchmarks, and reusable skills.
Set orchestration standards for scheduled model and agent workloads.
Establish observability for models and agents in production, covering data quality, drift, cost, and alerting.
Serve as technical design reviewer for pricing programs delivered by other teams.
Partner with machine learning engineering to deploy, version, and monitor production models.
Mentor data scientists and present technical work to leadership and business stakeholders.