Supply Chain Engineer - Loop

About Loop

Loop is on a mission to streamline the movement of money between shippers, carriers, and brokers to unlock margin and increase liquidity in the supply chain.

Today, logistics payments are a big black box that unnecessarily drains thousands, if not millions, of dollars. Legacy services do not meet the market's needs. Transportation teams need a solution that unlocks insights from their complex datasets, rather than drowning them in manual workflows.

Loop harnesses AI expertise with deep logistics industry knowledge to deliver automated audits, streamlined resolutions, expedited payments, and most importantly actionable insights. All so that shippers and 3PLs can improve spend visibility, eliminate unnecessary costs, and optimize their transportation spend. Loop’s customers see a 4% reduction in costs and an 80% boost in back-office productivity.

Loop is a customer-obsessed company that sees the complexity in the supply chain as an opportunity rather than a challenge. Our foundational AI capitalizes on the messiness in the system to maximize outcomes for our customers.

Investors include Founders Fund, 8VC, Susa Ventures, Flexport, and 50 industry-leading angel investors. Our team brings subject matter expertise and experience from companies like Uber, Google, Flexport, Meta, Samsara, Intuit, and Rakuten – as well as traditional logistics companies like CH Robinson.

About Role

As  an Supply Chain  Solutions Engineer at Loop, you will play a pivotal role in creating our data science organization. You’ll work cross-functionally to design and build the tools and reports that power our customer supply chains  such as  predictive analytics, center of gravity, network optimization, modal optimization, and much more. These products  will enable us and our clients to accelerate data-driven decision-making throughout. 

What you will do

• Own Core Supply Chain Tools: Design and build industry leading supply chain tools through client and internal engagement
• Own Core Reporting and Data Models: Collaborate with Analytics Engineers to derive and build scalable data models that power internal and client-facing analytics.
• Optimize Product: Continuously improve supply chain tools by leveraging AI and automation to enhance accuracy, efficiency, and client outcomes.

Qualifications:

• 5+ years Deep Supply Chain & Transportation Expertise: end-to-end supply chain operations, manufacturing, all  modes of transportation , warehousing, distribution, returns, and repair logistics. 
• 2+ years Advanced Analytics and Modeling Skills: network optimization, modal shifting, center of gravity, cost-to-serve, predictive analytics, and efficiency modeling
• Strong proficiency in SQL and Python for data transformations and automation
• Proven ability translating complex data into  actionable insights, through dashboards and reporting, at all levels within an organization for both internal and external stakeholders.
• Excellent business communication skills translating data insights into actionable business strategies

Nice to Have:

• Global Supply Chain experience
• Previous experience working in a data environment at a startup
• Experience working in a product-led or solutions engineering environment
• Interest or experience in working with cutting-edge open-source data tools
• Proven ability to convert ad-hoc data requests into scalable business solutions
• Familiarity with AI/ML model outputs and how to integrate them into analytics products

Who You Will Work With:

• EPD (Engineering, Product, Design, and Analytic Engineers): Partner across EPD to build and maintain the data artifacts that power data-driven product decisions.
• Strategy and Operations: Enable product delivery teams (solutioning, analyst) to deliver value to customers through actionable dashboards, metrics,  and self-service reports
• Product: Partner with product teams to ensure supply chain tools are shaped by real customer needs and delivered with impact
• AI Platform: Expedite the development of new models and training of existing models by simplifying the necessary data cleansing and preparation