Senior Data Scientist

Role Overview

Join Nash as its first Data Scientist. You’ll leverage logistics domain expertise and machine learning to build impactful data products across pricing, dispatch, and supply-demand forecasting. This is a high-ownership role partnering with multiple teams and delivering end-to-end data science and production systems.

Key Missions

• Identify and scope high-impact opportunities across logistics and marketplace operations.
• Own data science initiatives from discovery through iteration, deployment, and ongoing performance improvement.
• Build models that incorporate real-world logistics constraints and develop production data pipelines.

Responsibilities

• Work with large operational datasets.
• Develop and maintain production data pipelines.
• Translate business/customer outcomes into modeling and measurement.
• Operate in ambiguous environments with incomplete data.

Requirements

• 4+ years in a Data Scientist / Machine Learning Engineer / related quantitative role.
• Strong proficiency in Python and SQL.
• Experience with cloud data warehouses (e.g., Snowflake preferred).
• Experience taking projects from problem definition to production and measurement.
• Experience building and maintaining production ML systems (deep MLOps specialization not required).
• Domain experience in logistics, marketplaces, supply chain, or related areas with real-world constraints.
• Ability to communicate clearly with written/verbal clarity, including working with customers or senior stakeholders.
• Strong product judgment and ability to connect modeling decisions to business outcomes.

Nice to Have

• Experience with routing, ETA modeling, optimization algorithms, or geospatial data.
• Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models.
• Experience with supply-demand forecasting or marketplace balancing.
• Exposure to dbt, Airflow, or similar data orchestration tools.
• Experience deploying models via APIs or real-time decision systems.
• Prior experience at an early-stage company or in a founding data role.