Data Scientist II, Causal Inference, Trucking - Flexport

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 our competitive edge in a dynamic, two-sided marketplace.

You should have:  

• Advanced Degree and Quantitative Expertise: Ph.D. or Master’s in a quantitative field (e.g., Econometrics, Statistics, Economics, Applied Math, Operations Research, or Engineering) with 2+ years of post-graduate experience (with Masters) in a dynamic business environment, ideally in a marketplace or tech-driven setting.
  
  
• Technical Skills in Statistical and Machine Learning Tools: Proficiency in Python, SQL, for data analysis, modeling, and deployment, with hands-on experience applying these tools to build robust, interpretable models. You'll work with real-time bidding data from 100,000s of daily transactions!
  
  
• Experience with Causal Inference and Experimental Design: Strong foundation in causal inference and econometric techniques, with a proven track record of applying these frameworks to real-world problems. Experience designing and validating A/B testing, quasi-experimental methods, or impact analysis across complex systems is essential.
  
  
• Marketplace and Economic Insight: Understanding of two-sided marketplaces or similar systems where multi-agent interactions shape outcomes. Familiarity with economic theories or financial markets is a plus, as is experience with marketplace dynamics, such as broker and carrier engagement.
  
  
• Analytical Problem-Solving and Practicality: Ability to frame complex business problems as data solutions, with a practical, outcome-oriented approach. You excel at simplifying complex problems and are driven to see projects through from research to actionable insights.
  
  
• Strong Communication and Collaborative Skills: Excellent written and verbal communication skills, with the ability to convey complex statistical concepts to technical and non-technical audiences. Experience working cross-functionally with engineers, product managers, and other scientists to integrate data solutions within larger platform strategies.

Bonus Points

• 3+ years of post-graduate experience in a causal inference-focused role.
• Experience in high-performance computing or software development practices.
• Familiarity with deploying mature models in production environments and a track record of influencing cross-functional partners with data-driven insights.
• Publications or recognized contributions in causal inference or marketplace analytics.

Successful Flexporters share these traits:

• We’re passionate about solving complex problems and get excited about diving deep to understand an issue.
• Our titles don’t define us, we do what’s needed to prioritize our customers.
• We support others through change and uncertainty and stay the course when things get challenging.
• We believe in our mission and the potential for impact in our space

#LI-hybrid

Worried about not having any logistics experience? 

Don’t be! Our mission is to make global commerce so easy there will be more of it. That’s why it’s important to bring people from diverse backgrounds and experiences together with our industry veterans to help move the global logistics industry forward.

We know this industry is complex. That’s why we invest in education starting day one with Flexport Academy, a one week intensive onboarding program designed specifically to set every new Flexport employee up for success.