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Senior Data Scientist

NashSan FranciscoProduct and Engineering
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Salary
Not listed
Job type
Full-time
Work mode
Remote
Posted
30 Jul 2026
Status
Open · checked 9d ago

About this role

The Senior Data Scientist role at Built for the reality of logistics in San Francisco offers a unique opportunity to build data products from discovery through production. This high-ownership position suits an experienced quantitative professional who thrives on tackling complex real-world challenges in logistics and marketplaces.

Role Overview

As the first Data Scientist for the organization, the individual in this role will combine product judgment, logistics expertise, and practical machine learning skills. Responsibilities span across pricing, dispatch, carrier selection, ETA prediction, routing, and supply-demand forecasting. Working directly with engineering, product, operations, leadership, and enterprise clients, the professional will turn ambiguity into measurable outcomes that influence millions of deliveries.

Key Responsibilities

Identify and scope high-impact opportunities across pricing, cost prediction, dispatch, carrier selection, ETA prediction, routing, and marketplace balancing.
Own data science initiatives completely, moving from initial 0 to 1 discovery through 1 to 10 iteration, deployment, and performance enhancement.
Handle large and complex operational datasets that include delivery events, geospatial data, carrier performance, customer constraints, and service level agreement outcomes.
Build machine learning models that account for shifting demand, real-world logistics constraints, provider availability, and specific service requirements.
Develop production data pipelines and model integrations utilizing Python, SQL, and Snowflake.
Collaborate closely with engineers to serve models through APIs, batch pipelines, or real-time decision systems.
Establish robust evaluation frameworks, monitoring, experimentation, and A/B testing practices.
Measure model performance against key business outcomes like cost, reliability, on-time delivery, and the need for operational interventions.
Partner directly with enterprise customers to translate business operations and requirements into technical approaches.
Communicate research findings, technical tradeoffs, and strategic recommendations clearly to both technical and non-technical stakeholders.

Requirements

Minimum of 4 years of professional experience as a Data Scientist, Machine Learning Engineer, or in a related quantitative role.
Practical background in logistics, marketplaces, supply chain, operations research, or other complex real-world constrained domains.
A proven track record of independently taking data science projects from problem definition through production and performance measurement.
Strong proficiency in Python and SQL, accompanied by practical experience working within cloud data warehouses such as Snowflake.
Experience in building and maintaining production machine learning systems.
Strong product judgment with the ability to connect quantitative modeling decisions directly to customer and business results.
Comfort operating with incomplete data, ambiguous questions, and shifting operational environments.
Clear written and verbal communication skills, including direct experience working with senior stakeholders or enterprise customers.
High agency and a proactive mindset in a fast-moving environment.

Nice to Have

Experience with routing, ETA modeling, optimization algorithms, or geospatial data handling.
Familiarity with dispatch systems, carrier networks, logistics marketplaces, or pricing models.
Background in supply-demand forecasting or marketplace balancing.
Exposure to data orchestration tools such as dbt or Airflow.
Experience deploying models through APIs or real-time decision systems.
Prior professional experience working at an early-stage company or serving in a founding data role.

Skills and Tools

Python
SQL
MLOps
Airflow
Snowflake
dbt

Salary and Benefits

Competitive compensation package including meaningful equity
Flexible paid time off policy
Comprehensive health, dental, and vision insurance for US employees
Quarterly team on-sites for in-person collaboration and connection

About the Company

Headquartered in San Francisco and founded in 2021 by Mahmoud Ghulman and Aziz Alghunaim, Built for the reality of logistics is an autonomic logistics platform. Backed by prominent investors like Y Combinator, a16z, and OpenAI, the company unifies decisioning and execution across various fleets, carriers, and fulfillment networks. Major global retailers, grocers, and pharmacies rely on the platform to run critical logistics workflows.

How to Apply

Interested candidates should visit the official company careers page to submit an application for this remote or San Francisco-based position.

Skills and tech stack

PythonSQLMLOpsAirflowSnowflakedbt

Questions candidates ask

What is the location requirement for this role?

The position is based in San Francisco with remote flexibility available.

How much experience is required to apply?

Candidates must have 4+ years of experience as a Data Scientist, Machine Learning Engineer, or in a related quantitative role.

What core technical skills are needed?

The role requires strong proficiency in Python and SQL, along with experience working in cloud data warehouses like Snowflake.

Is deep MLOps specialization required?

No, a deep MLOps specialization is not required, though experience building and maintaining production machine learning systems is necessary.

What are some of the bonus qualifications for this position?

Bonus qualifications include experience with routing, ETA modeling, geospatial data, optimization algorithms, dispatch systems, dbt, Airflow, and early-stage startup environments.

Ready to apply?

The application is on the official Nash careers page.

Apply on company site