The ad says“Strong proficiency in Python and SQL, with experience working in cloud data warehouses. Snowflake experience is preferred”
The ad says“Strong proficiency in Python and SQL, with experience working in cloud data warehouses. Snowflake experience is preferred”
Values
The ad says“Exposure to dbt, Airflow, or related data orchestration tools”
The ad says“Experience deploying models through APIs or real-time decision systems”
The ad says“Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models”
The ad says“Exposure to dbt, Airflow, or related data orchestration tools”
The ad says“Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models”
The ad says“Experience with routing, ETA modeling, optimization algorithms, or geospatial data”
The ad says“Experience with routing, ETA modeling, optimization algorithms, or geospatial data”
The ad says“Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models”
The ad says“Experience with supply-demand forecasting or marketplace balancing”
The ad says“Experience building and maintaining production machine learning systems. Deep MLOps specialization is not required”
The ad says“Experience with routing, ETA modeling, optimization algorithms, or geospatial data”
The ad says“Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models”
The ad says“Experience with routing, ETA modeling, optimization algorithms, or geospatial data”
The ad says“Strong proficiency in Python and SQL, with experience working in cloud data warehouses. Snowflake experience is preferred”
The ad says“Experience with supply-demand forecasting or marketplace balancing”
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
Preferred experience
High agency in a fast-moving environment. You notice valuable problems and act on them
Clear written and verbal communication, including experience working with customers or senior stakeholders
Experience in logistics, marketplaces, supply chain, operations research, or another domain with complex real-world constraints
4+ years of experience as a Data Scientist, Machine Learning Engineer, or in a related quantitative role
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
Strong proficiency in Python and SQL, with experience working in cloud data warehouses. Snowflake experience is preferred
A record of independently taking data science projects from problem definition through production and measurement
Experience building and maintaining production machine learning systems. Deep MLOps specialization is not required
Experience with supply-demand forecasting or marketplace balancing
Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models
Experience with routing, ETA modeling, optimization algorithms, or geospatial data
Experience deploying models through APIs or real-time decision systems
Exposure to dbt, Airflow, or related data orchestration tools
Prior experience at an early-stage company or in a founding data role
