Tesla

Makes electric cars, robotaxis and grid batteries

Data Statistician

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeFremont, CA, USANo salary listedIn-person

About the Job

Requirements

A degree in Data Science, AI, Applied Math, or Operations Research, or equivalent experience.
A strong foundation in mathematics, algorithms, and optimization techniques.
Expertise in large language models, autonomous AI agents, prompt engineering, and modern AI frameworks.
Strong programming proficiency in Python and SQL, with experience using NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.
Hands-on experience developing full-stack applications using Django (Python), Java-based services, and React, with a solid understanding of RESTful API design, integration, and service-oriented architectures.
Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and familiarity with containerization and orchestration tools such as Docker and Kubernetes.

Responsibilities

Build and maintain ETL pipelines and recurring analytics to support inbound and service parts operations decision-making.
Conduct deep-dive analyses on supply chain performance, including benchmarking, pattern and bottleneck diagnosis, and scenario modeling.
Develop and integrate Digital Twin capabilities that unify inbound supply, outbound demand, logistics constraints, and warehouse capacity, contributing to feedback loops, data validation, and platform testing.
Architect, own, and operate end-to-end production ML pipelines, including CI/CD, model versioning, and orchestration, to ensure scalable, modular, reliable, and high-performance inventory optimization systems.
Design and implement ML models to analyze bottlenecks for Service Parts Operations, specifically on prep performance and diagnosis.
Deliver multi-model finance optimizations that integrate cost strategies, simulate disruptions, and recommend initiatives to improve overall throughput and productivity while maintaining low costs.
Support engagement and efficient integration of Supply Chain tools within the network.
Translate complex quantitative results into clear narratives and visualizations, enabling leadership to make informed tradeoffs between inventory investment, service levels, and operational complexity across the global supply chain.

Category	Supply Chain
Location	Fremont, California
Requisition ID	282286
Type	Full-time

Job Description

We are seeking a highly skilled and driven Data Statistician with software skills to help us develop the next Supply Chain platform with a focus on Inbound analytics, agent development and day-to-day operations. You will be part of the Supply Chain Optimization team that is managing strategic projects and continuous improvement efforts for Tesla’s Global Distribution Network. The role requires the ability to build strong cross-functional working relationships with Material Planning, Logistics, Finance, Warehouse Operations and Service Operations teams. You will require a sharp business focus, a collaborative style of working, and a proactive and critical mindset. You need to acquire deep subject matter knowledge about systems, sourcing, planning and fulfillment processes to build models that drive impactful business changes. The role is expected to simultaneously handle multiple projects of department-level scale and global reach. This role is a strong fit for someone early in their career who is eager to build deep operational knowledge and grow into a ML engineering role.

Job Responsibilities

Build and maintain ETL pipelines and recurring analytics to support inbound and service parts operations decision-making

Conduct deep-dive analyses on supply chain performances, including benchmarking, patterns and bottlenecks diagnosis as well as scenario modeling

Develop and integrate Digital Twin capabilities that unify inbound supply, outbound demand, logistics constraints, warehouse capacity, contributing to feedback loops, data validation and platform testing

Architect, own, and operate end-to-end production ML pipelines, including CI/CD, model versioning, and orchestration, to ensure scalable, modular, reliable, and high-performance inventory optimization systems

Design and implement ML models to analyze bottlenecks for Service Parts Operations, specifically on prep performance and diagnosis

Deliver multi-model finance optimizations that integrate cost strategies, simulate disruptions and recommend initiatives to improve overall throughput and productivity while maintaining costs low

Support engagement and efficient integration of Supply Chain tools within the network

Translate complex quantitative results into clear narratives and visualizations, enabling leadership to make informed tradeoffs between inventory investment, service levels, and operational complexity across the global supply chain

Job Requirements

Degree in Data Science, AI, Applied Math or Operations Research, or equivalent experience

Strong foundation in mathematics, algorithms, and optimization techniques

Expertise in large language models, autonomous AI agents, prompt engineering, and modern AI frameworks

Strong programming proficiency in Python and SQL, with experience using libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch

Hands-on experience developing full-stack applications using Django (Python), Java-based services, and React, with a solid understanding of RESTful API design, integration, and service-oriented architectures

Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and familiarity with containerization and orchestration tools such as Docker and Kubernetes

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

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