Data Scientist II, Causal Inference, Trucking

About Flexport: 

At Flexport, we believe global trade can move the human race forward. That’s why it’s our mission to make global commerce so easy there will be more of it. We’re shaping the future of a $8.6T industry with solutions powered by innovative technology and exceptional people. Today, companies of all sizes—from emerging brands to Fortune 500s—use Flexport technology to move more than $19B of merchandise across 112 countries a year. 

The recent global supply chain crisis has put Flexport center stage as we continue to play a pivotal role in how goods move around the world. At a valuation of $8 billion, we are proud to have the support of the best investors in the game who believe in our mission, solutions and people. Ready to tackle global challenges that impact business, society, and the environment? Come join us.

The opportunity: 

Flexport Trucking is looking for an analytically-driven, technically-skilled Data Scientist with a background in causal inference to tackle some of the most complex problems in the trucking industry.

Flexport Trucking’s Convoy Platform is the nation’s leading digital freight marketplace, providing brokerages with an ecosystem of tools for connecting with carriers and managing their logistics needs. Brokers post loads, carriers interact through our app to view, bid on, and secure those loads, and Convoy Platform’s technology ensures the right carrier finds the right load with the right pricing. Our interconnected network enables the movement of millions of truckloads across the country, enhancing earnings for carriers and brokers alike while minimizing waste and maximizing efficiency.

Data Science is central to Convoy Platform. Our team builds the models that power critical functions: from freight pricing and load relevance engines to auction strategies, fraud detection, safety scoring, and ETA predictions. We use causal inference to evaluate the impact of business decisions, model marketplace dynamics, and optimize core services for pricing, load-carrier relevance, and customer experience. Our platform constantly adapts, with manual and automated interventions, evolving economic inputs and market signals from brokers, etc. The breadth of the problem space is vast, spanning machine learning, optimization, experimental design, and econometrics. We’ve only scratched the surface of what’s possible within this complex, two-sided marketplace.

We are seeking a Causal Inference Data Scientist to contribute to the development of data-driven insights within our marketplace, including evaluating the causal impact of new pricing models, analyzing the effects of AI-driven marketing campaigns on carrier engagement, and refining algorithms to maximize both load coverage and broker margins. Join us in solving meaningful, challenging problems that sit at the heart of our marketplace.

You will:

• Model Marketplace Dynamics with Causal Inference: Build models that capture the causal impact of decisions within our two-sided marketplace. Using econometric frameworks and causal inference techniques, you’ll quantify how actions from different brokers (e.g., rate changes, cancellations) affect carrier engagement, churn, and auction results, recommending targeted actions based on these insights.
  
  
• Enhance Experimental Rigor in Product Development: Conduct A/B testing and online experiments to add scientific rigor to feature releases across our platform. Guide the organization’s learning processes by setting up experiments that reveal the causal effects of different interventions—such as optimizing communication channels to reduce carrier falloffs or testing alternative pricing adjustments to sustain broker engagement.
  
  
• Collaborate on Cross-Functional Innovation: Work closely with product managers, engineers, and other scientists to ensure seamless integration of your causal models and algorithms within the platform. Develop feedback-aware decision systems capable of ingesting human and automated feedback to continually improve performance, auction parameters, and load recommendations.
  
  
• Analyze Market Conditions and Broker Heterogeneity: As our marketplace expands, different broker actions (e.g., variable pricing strategies, and multi-broker lane engagement) will create complex interactions. You will investigate these dynamics and help our team quickly identify impacts on carrier behavior, enabling us to refine auction mechanisms and improve our relevance engines with actionable insights.
  
  
• Stay Ahead in Causal Inference and Market Analytics: Leverage advancements in causal inference, machine learning, and econometrics to refine our experimentation platform, implement innovative decision-making frameworks, and enhance