Senior Data Scientist

About Nash

Nash is the autonomic logistics platform. We unify decisioning and execution across fleets, carriers, providers, and fulfillment networks, continuously adapting as conditions change and pursuing the best possible outcome for each business.

Department: Product and Engineering

Location: San Francisco, Remote HQ

Employment Type: Full Time

Nash is the autonomic logistics platform. We unify decisioning and execution across fleets, carriers, providers, and fulfillment networks, continuously adapting as conditions change and pursuing the best possible outcome for each business.

The world’s largest retailers, grocers, and pharmacies, including Walmart, 7‑E‑leven, Woolworths, and Coles, run critical logistics workflows on Nash.

Nash was founded in 2021 by Mahmoud Ghulman and Aziz Alghunaim. We are backed by Y Combinator, a16z, OpenAI, and other leading investors, and headquartered in San Francisco.

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.

• 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.

• 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.
• Prio…