Senior Data Scientist

Arizona Public Service - APS

Data Scientist/ Senior Data Scientist at APS leading production-grade machine learning systems. Collaborating cross-functionally and driving MLOps initiatives for enterprise solutions.

Posted 6/16/2026full-timePhoenix • Arizona • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Lead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable, reliable, and secure.
Architect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelines and streaming environments.
Advise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deep domain expertise in ML model design and infrastructure.
Collaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists to deliver real-world business value.
Own Quality & Governance: Establish and maintain best practices for ML lifecycle management, including CI/CD, monitoring, testing, and documentation.

Requirements

What you’ll need

BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field PLUS minimum four (4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role OR advanced degree and two (2) years directly related experience.
Deep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g., Kubeflow, SageMaker, MLflow).
Proven ability to design scalable ML architectures for streaming and batch use cases.
Sponsorship for U.S. work authorization is not available for this position, now or in the future.

Benefits

Comp & perks

Support for veterans and spouses
Flexible work arrangements

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdata analyticspredictive modelingstatistical modelingML model designML lifecycle managementCI/CDscalable ML architecturesdata pipelinesstreaming environments

Soft Skills

collaborationthought partnershipinnovationadvisingcross-functional teamwork