Data Scientist II — ML Platform & GenAI Innovator

H.E.B.; San Antonio (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

H-E-B is seeking a Senior Data Scientist to design and maintain an ML platform and related environments, including orchestration of Docker and Kubernetes, CI/CD pipelines for model deployment, and ongoing optimization of training performance.

You will collaborate with data scientists, engineers, and stakeholders to translate business questions into ML solutions, architect GenAI, and handle diverse data types for robust Language Model learning.

Qualifications

• Related degree or comparable formal training, certification, or work experience.
• 5+ years of experience in a retail or retail-related decision science role.
• Expertise in ML visualization flow.
• Expertise in optimizing distributed machine learning in a heterogeneous domain environment.

Responsibilities

• Design, create, and maintain an ML platform and related environments.
• Manage Docker containers and Kubernetes clusters, CI/CD pipelines for ML models.
• Monitor and optimize model training performance and resource usage.
• Deploy ML models to production and manage versioning and rollback.
• Ensure scalable model serving using Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI.
• Collaborate with data scientists, data engineers, and stakeholders to fulfill infrastructure needs.
• Architect and develop Generative AI solutions focusing on Retrieval-Augmented Generation (RAG) systems, search, knowledge graphs, and multi-agent workflows.
• Handle unstructured and structured data, prepare it for Language Model Learning, and build connectors to databases.
• Train models on prepared data and fine-tune hyperparameters.

Skills

SQL

Python

R

Scala

Java

C/C++

Machine Learning

Big Data

NLP

TensorFlow

Education

Related degree

Tools

TensorFlow Serving

Databricks

Kubernetes

Docker

Job description

H-E-B is seeking a Senior Data Scientist to design and maintain an ML platform and related environments, including orchestration of Docker and Kubernetes, CI/CD pipelines for model deployment, and ongoing optimization of training performance.

You will collaborate with data scientists, engineers, and stakeholders to translate business questions into ML solutions, architect GenAI, and handle diverse data types for robust Language Model learning.