Enterprise Data Science Architect & ML Leader

Ethereum Technologies; Dallas (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Ethereum Technologies in Dallas, TX seeks a Data Science Architect to design and lead enterprise-scale AI/ML solutions. You will define scalable data architectures, ML pipelines, and MLOps practices across cloud platforms (AWS/Azure/GCP) and collaborate with data scientists, engineers, and stakeholders.

The role emphasizes governance, security, and performance, with hands-on work in Python, Spark, Databricks, and model deployment, while mentoring teams and shaping technical standards for the

Qualifications

• 9+ years of experience in Data Science, ML, AI, or related tech disciplines.

Responsibilities

• Design end-to-end Data Science, ML, and AI architectures for enterprise applications.
• Define scalable architectures for data ingestion, processing, analytics, ML, and model deployment.
• Lead the architecture and implementation of predictive analytics, ML models, and advanced analytics solutions.
• Collaborate with Data Scientists and Data Engineers to develop robust data pipelines and platforms.
• Establish standards for model development, validation, deployment, monitoring, and lifecycle management.
• Design and implement MLOps processes and CI/CD pipelines for ML workloads.
• Architect cloud-based data science solutions on AWS, Azure, or GCP.
• Work with Python, SQL, Spark, Databricks, MLflow, TensorFlow, PyTorch, and Scikit-learn.
• Ensure data governance, security, privacy, and enterprise architecture principles.

Skills

Python

SQL

Data modeling

Machine learning

MLOps

CI/CD

Data pipelines

Cloud platforms

Tools

Spark

Databricks

TensorFlow

PyTorch

Scikit-learn

XGBoost

Docker

Kubernetes

REST APIs

Terraform

Git

Job description

Ethereum Technologies in Dallas, TX seeks a Data Science Architect to design and lead enterprise-scale AI/ML solutions. You will define scalable data architectures, ML pipelines, and MLOps practices across cloud platforms (AWS/Azure/GCP) and collaborate with data scientists, engineers, and stakeholders.

The role emphasizes governance, security, and performance, with hands-on work in Python, Spark, Databricks, and model deployment, while mentoring teams and shaping technical standards for the