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Nash is an autonomic logistics platform that unifies decisioning and execution across fleets, carriers, providers, and fulfillment networks. Founded in 2021 and backed by Y Combinator, a16z, and OpenAI, Nash serves major retailers including Walmart, 7-Eleven, Woolworths, and Coles, influencing real-world decisions across millions of deliveries.
You will be Nash's first Data Scientist, combining product judgment, logistics expertise, and pragmatic machine learning to build data products from discovery through production and measurement. This is a high-ownership role requiring the ability to find valuable problems, turn ambiguity into measurable outcomes, and build models and systems that improve outcomes in production.
Key responsibilities include identifying and scoping high-impact opportunities across pricing, cost prediction, dispatch, carrier selection, ETA prediction, routing, and marketplace balancing. You will own data science initiatives end-to-end, from 0→1 discovery through iteration, deployment, and performance improvement. You'll work with large, messy operational datasets including delivery events, geospatial data, carrier performance, and SLA outcomes.
You will build models accounting for real-world logistics constraints, shifting demand, provider availability, and service requirements. Development will involve Python, SQL, and Snowflake for production data pipelines and model integrations. You'll partner with engineers to serve models through APIs, batch pipelines, or real-time decision systems, and establish evaluation frameworks, monitoring, experimentation, and A/B testing practices.
Measurement focuses on business outcomes: cost, reliability, on-time delivery, and operational intervention. You'll work directly with enterprise customers to understand operations and convert business requirements into technical approaches, communicating findings and recommendations to technical and non-technical audiences.
Required: 4+ years as a Data Scientist, Machine Learning Engineer, or related quantitative role; experience in logistics, marketplaces, supply chain, or operations research; proven track record taking projects from problem definition through production; strong Python and SQL proficiency with cloud data warehouse experience (Snowflake preferred); production ML systems experience; strong product judgment; comfort with incomplete data and ambiguous questions; clear communication skills; high agency in fast-moving environments.
Bonus qualifications include routing, ETA modeling, optimization algorithms, geospatial data experience; familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models; supply-demand forecasting or marketplace balancing experience; exposure to dbt or Airflow; model deployment through APIs or real-time systems; early-stage or founding data role experience.
