Nash - Senior Data Scientist
Requirements
• 4+ years of experience as a Data Scientist, Machine Learning Engineer, or in a related quantitative role. • Experience in logistics, marketplaces, supply chain, operations research, or another domain with complex real-world constraints. • A record of independently taking data science projects from problem definition through production and measurement. • Strong proficiency in Python and SQL, with experience working in cloud data warehouses. Snowflake experience is preferred. • Experience building and maintaining production machine learning systems. Deep MLOps specialization is not required. • Strong product judgment and the ability to connect modeling decisions to customer and business outcomes. • Comfort working with incomplete data, ambiguous questions, and changing operational conditions. • Clear written and verbal communication, including experience working with customers or senior stakeholders. • High agency in a fast-moving environment. You notice valuable problems and act on them. • Bonus • 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 related data orchestration tools. • Experience deploying models through APIs or real-time decision systems. • Prior experience at an early-stage company or in a founding data role.
Responsibilities
• Identify and scope high-impact opportunities across pricing, cost prediction, dispatch, carrier selection, ETA prediction, routing, and marketplace balancing. • Own data science initiatives from 0→1 discovery through 1→10 iteration, deployment, and performance improvement. • Work with large, messy operational datasets, including delivery events, geospatial data, carrier performance, customer constraints, and SLA outcomes. • Build models that account for real-world logistics constraints, shifting demand, provider availability, and service requirements. • Develop production data pipelines and model integrations using Python, SQL, and Snowflake. • Partner with engineers to serve models through APIs, batch pipelines, or real-time decision systems. • Establish evaluation frameworks, monitoring, experimentation, and A/B testing practices. • Measure model performance against business outcomes such as cost, reliability, on-time delivery, and operational intervention. • Work directly with enterprise customers to understand their operations and convert business requirements into technical approaches. • Communicate findings, tradeoffs, and recommendations clearly to technical and non-technical audiences.
Benefits
• The decisions Nash makes affect what a delivery costs, which resource handles it, when it arrives, and whether the customer’s promise holds when conditions change. • As our first Data Scientist, you will define how Nash uses operational data to make those decisions sharper. The models you build will move quickly from analysis into live logistics workflows, giving you a direct view into their customer and business impact.
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