Data Scientist – Transportation & Logistics Intelligence

About the Role:

ShipMonk has built world-class infrastructure for shipping—our Virtual Carrier Network (VCN), claims systems, and logistics modeling are the rails. Now, we need the engine. We are seeking a Data Scientist – Transportation & Logistics Intelligence to transform our transportation systems to "predictive-first."

In this role, you won’t just be "analyzing data"—you’ll be building the autonomous logic that powers millions of shipments. You are the architect who will replace manual "if/then" rules with real-time, high-margin predictive models. Your mission is to bridge the gap between logistics physics and machine learning, ensuring that manual becomes modeled and reactive becomes predictive.

Desired Experience:

• Education: Master’s or PhD in Data Science, Statistics, Operations Research, or a related quantitative field (or equivalent practical experience).
• Data Core: 4+ years of experience building and deploying predictive models into production environments.
• Data Engineering: Strong proficiency with dbt and SQL within a Snowflake (or BigQuery/Redshift) environment. You should be comfortable collaborating on complex DAGs and version-controlled data pipelines.
• Logistics DNA: Previous experience in transportation, supply chain, or e-commerce logistics is a massive plus. You should understand the nuances of carrier performance, transit latencies, and "last-mile" variables.
• The Stack: Expert-level proficiency in Python and SQL. Experience with machine learning frameworks (e.g., Scikit-learn, XGBoost, PyTorch) and cloud-based data warehouses (Snowflake, BigQuery).
• Business Acumen: Ability to translate complex model outputs into "Margin impact" or "Conversion growth" for stakeholders.

Skills & Responsibilities:

• VCN Optimization (The Margin Engine): Build real-time risk/value models. You’ll build the logic that weighs package value vs. risk of loss to ensure we never overspend on "safe" routes or under-insure "risky" ones.
• Precision EDD (The Conversion Driver): You will develop dynamic Estimated Delivery Date (EDD) models that adjust in real-time based on live network health, weather, and historical carrier latency.
• Predictive "At-Risk" Intelligence: Build the "early warning system" for our warehouse and support teams. You’ll develop models that identify "lost" or delayed packages before a merchant or customer even notices—enabling proactive resolution and automated claims.
• Collaborative Data Preparation: Partner with Engineering to co-author and refine the dbt models and SQL transformations that power your work. You’ll ensure the "preparation layer" is optimized for feature engineering and model training.
• Scalable Modeling: Transform manual logistics workflows into autonomous systems. You’ll be responsible for ensuring our intelligence scales with our global warehouse footprint.
• Cross-functional Collaboration: Partner with Product Managers and Engineering teams to integrate your models directly into the ShipMonk application, moving from "insights on a slide" to "logic in the code."
• A/B Testing & Validation: Design and execute experiments to validate that your models are actually driving the expected increases in margin and delivery reliability.