Sr. Data Analyst, Machine Learning

Role overview

Tesla’s Supply Chain Analytics & Engineering organization is hiring a senior data analyst to strengthen predictive analytics and machine learning across its global supply chain function. The position is focused on building, scaling, and improving ML solutions that support major supply chain decisions, while also working with Data Engineering to design AI applications and partnering with analysts to uncover new opportunities for model-driven improvements.

The role requires regular interaction with stakeholders at all levels, including senior leadership, so the ability to explain technical results in practical business terms is essential. The right candidate will bring strong machine learning expertise, a self-driven approach, and a history of turning data science work into measurable operational impact in a fast-moving environment.

Key work areas

• Partner with the Data Engineering team to shape AI applications, define model needs, engineer features, and manage the full machine learning lifecycle from development through deployment and ongoing monitoring.
• Build and support scalable ML pipelines covering data preparation, feature creation, model training, evaluation, and serving infrastructure.
• Turn analytical output into clear recommendations for Supply Chain leadership and other stakeholders, helping move the team from descriptive reporting toward predictive and prescriptive decision support.
• Assess new machine learning and AI methods and tools, adopting useful approaches that improve performance and broaden the team’s data science capabilities.
• Work across functions to ensure data science solutions are aligned with business priorities and deliver measurable results.

Required background and capabilities

• A degree in Data Science, Machine Learning, Statistics, Computer Science, or another quantitative discipline, or equivalent experience that demonstrates exceptional ability.
• 3 to 5 years of hands-on experience in applied data science or machine learning, including building, deploying, and maintaining production models.
• Strong Python skills and familiarity with ML libraries such as scikit-learn, XGBoost, TensorFlow, and PyTorch.
• Practical experience with clustering, classification, regression, time-series forecasting, and NLP for real business problems.
• Solid SQL ability for extracting, transforming, and analyzing large datasets.
• Experience with MLOps practices such as version control for models, experiment tracking with tools like MLflow, automated retraining, and drift monitoring.
• Exposure to workflow orchestration tools such as Airflow and containerized deployment technologies like Docker and Kubernetes.
• Strong written and verbal communication skills, with the ability to present technical findings to non-technical audiences and collaborate effectively across teams.
• Supply chain, manufacturing, or procurement analytics experience is an advantage.

Compensation and benefits

The posted annual pay range is $120,000 to $216,000, with additional cash awards, stock awards, and benefits. Final pay may differ based on factors such as location, relevant knowledge, skills, and experience.

As a full-time employee, benefits are available starting on the first day of employment and include medical plan options with $0 payroll deduction, family-building support, fertility, adoption and surrogacy benefits, dental and vision coverage with $0 paycheck contribution options, HSA contributions for eligible high-deductible medical coverage, FSA options, 401(k) with employer match, employee stock purchase plans, company-paid basic life and AD&D, short- and long-term disability insurance with a 90-day waiting period, an employee assistance program, sick and vacation time, paid holidays, backup childcare and parenting support, voluntary insurance and legal/pet-related benefits, weight loss and tobacco cessation programs, the Tesla Babies program, commuter support, and employee discounts/perks.