Job description

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. 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 ROLE 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. 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. BONUS - 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. - Prior experience at an early-stage company or in a founding data role. WHY THIS ROLE MATTERS The decisions Nash makes affect what a delivery costs, which resource handles it, when it arrives, and whether the customer’s promise holds when conditions change. As our first Data Scientist, you will define how Nash uses operational data to make those decisions sharper. The models you build will move quickly from analysis into live logistics workflows, giving you a direct view into their customer and business impact. WHAT YOU’LL LOVE ABOUT NASH - An early-stage, well-funded company with real revenue and global enterprise customers - Significant ownership and autonomy, with direct collaboration with the founders - Quarterly team onsites to connect and align in person - Competitive compensation and meaningful equity - Flexible paid time off - Health, dental, and vision insurance EQUAL OPPORTUNITY At Nash, we believe diverse teams are the strongest teams. We invite applicants of all genders, races, ethnicities, nationalities, ages, religions, sexual orientations, disability statuses, educational experiences, family situations, and socioeconomic backgrounds. More about Nash nash.ai NASH IS THE PLATFORM THAT POWERS MODERN LOGISTICS. Commerce has inverted. For decades, customers came to where products and services were. Now products and services come to them, on their terms, in real time. That shift has turned every company into a logistics company, even though almost none of them were built to be one. Couriers, fleets, gig workers, parcel carriers, in-store labor, and increasingly autonomous systems all have to be coordinated in real time, against tighter windows and rising expectations, with hard-fought customer trust on the line. Nash unifies decisioning, execution, and capacity into a single programmable platform. Real-time, AI-native intelligence determines what should happen, operational control executes it, and the platform dynamically orchestrates capacity from any source: a company's own fleets, partners, or the Nash delivery network. Whether a job involves a courier, a gig driver, an internal fleet, a store employee, a technician, or an autonomous vehicle, Nash selects the right resource and manages execution through completion. We power delivery and logistics for some of the most recognizable brands in commerce, including Walmart, Urban Outfitters, 7-Eleven, and Woolworths, alongside platforms like Shopify and Toast. Over the next decade, logistics will become as foundational to commerce as payments, cloud, and connectivity. Nash is the platform that powers it. Nash was founded in 2021 by Mahmoud Ghulman (2x Founder, MIT) and Aziz Alghunaim (2x Founder, 2x YC, Ex-Palantir, MIT) and is backed by Y Combinator, a16z, and other top investors. We are headquartered in San Francisco. WHAT YOU’LL LOVE ABOUT US ✅ Early-stage, well-funded startup – directly impact the company and grow your career! ✅ Quarterly broader team on-sites to bond with teammates ✅ Competitive compensation and opportunity for equity ✅ Flexible paid time off ✅ Health, dental, and vision insurance for US employees

Requirements

Must-haves

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.
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.

Nice-to-haves

Strong proficiency in Python and SQL, with experience working in cloud data warehouses. Snowflake experience is preferred.
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.
Prior experience at an early-stage company or in a founding data role.

Application prep

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Keywords to mirror

machine learningpythonsqlai

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off