Senior Data Scientist, Nash

• 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

Desirable

• We are hiring Nash’s first Data Scientist. You will combine product judgment, logistics or marketplace expertise, and pragmatic machine learning skills to build data products from discovery through production and measurement
• You will work across pricing, dispatch, carrier selection, ETA prediction, routing, and supply-demand forecasting
• This is a high-ownership role for someone who can find valuable problems, turn ambiguity into measurable outcomes, and build the models and systems needed to improve those outcomes in production
• You will partner directly with Product, Engineering, Operations, customers, and company leadership
• 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