Senior Data Scientist

We Love AI Jobs; San Francisco (CA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Nash is hiring its first Data Scientist to build and productionize models for logistics decisions including pricing, dispatch, routing, and ETA prediction. You will work with operational data and partner with product, engineering, and leadership to measure business impact on a hybrid team in San Francisco.

You will own data science initiatives, develop production pipelines, and apply rigorous evaluation to drive cost efficiency and reliable deliveries at scale.

Qualifications

• 4+ years of experience in data science, ML engineering, or related quantitative role.
• Strong proficiency in Python and SQL, with cloud data warehouses experience.
• Experience building and maintaining production ML systems and deploying models.

Responsibilities

• Identify and scope high-impact opportunities across pricing, dispatch, routing, and marketplace balancing.
• Own data science initiatives from discovery through production, deployment, and measurement.
• Develop production data pipelines and model integrations using Python, SQL, and Snowflake.
• Establish evaluation, monitoring, experimentation, and A/B testing practices.
• Communicate findings and recommendations clearly to technical and non-technical stakeholders.

Skills

Python

SQL

Machine learning

Snowflake

Data pipelines

Model evaluation

A/B testing

Geospatial data

Tools

Snowflake

Job description

# Senior Data ScientistNashSan Francisco (Hybrid)Full-timeUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageAs Nash’s first Data Scientist, you’ll build and productionize models for logistics decisions such as pricing, dispatch, routing, and ETA prediction. You’ll work with operational data and partner with product, engineering, operations, customers, and leadership to measure business impact.* Permanent* Hybrid* 4+ years### Skills and expertisePythonSQLMachine learningSnowflakeData pipelinesModel evaluationA/B testingGeospatial data### Key responsibilities* Identify and scope valuable modeling opportunities across pricing, dispatch, routing, and marketplace operations.* Build, deploy, and improve production models using logistics data and real-world constraints.* Establish evaluation and monitoring practices, and measure model performance against business outcomes.## How this job comparesCompared with 269 open senior data jobs on October 10, 2026.Pay: This job does not publish its pay. The 82 open senior data jobs that publish US pay start at $157K a year on average.Similar open jobs: We Love AI Jobs lists 269 open senior data jobs at 124 companies.Remote work: This job is hybrid. 60 of the 269 senior data jobs (22%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. Building and deploying production machine learning models is the core of the role. The models inform live logistics decisions, including dispatch, carrier selection, pricing, and ETA prediction.## Full descriptionSenior Data ScientistAbout NashNash 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-Eleven, Woolworths, and Coles, run critical logistics workflows on Nash. Your work will influence real-world decisions across millions of deliveries.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.About the roleWe 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.What you’ll do• 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.What you’ll bring• 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.