H-E-B
Overview
Join to apply for the
Sr Data Scientist - ML Engineering
role at
H-E-B Join to apply for the
Sr Data Scientist - ML Engineering
role at
H-E-B Responsibilities
Design, create, and maintain an ML platform and related environments. Manage Docker containers and Kubernetes clusters, oversee dependencies and configurations, and implement CI/CD pipelines for automated building, testing, and deployment of machine learning models. Monitor and optimize model training performance and resource usage. Deploy ML models to production environments and manage model versioning and rollback mechanisms. Ensure scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet growing demands as the number and complexity of ML models increase. Work closely with data scientists, data engineers, and stakeholders to understand and fulfill infrastructure needs; stay updated with the latest ML infrastructure technologies and best practices. Architect and develop Generative AI solutions utilizing ML and GenAI techniques; collaborate with leadership to identify AI opportunities and promote AI strategy; specialize in engineering and deploying Generative AI models, with a focus on Retrieval-Augmented Generation (RAG) systems, search, knowledge graphs, and multi-agent workflows. Handle both unstructured and structured data, preparing it to be used as context for Language Model Learning (LLM); tasks include embedding large text corpora, developing generative SQL queries, and building connectors to structured databases. Train models on prepared data and fine-tune hyperparameters to achieve optimal performance. Analytics / Design & Development
Build a framework to stitch cross-domain learning and optimize them toward mission-specific and multi-mission tasks. Serve as an expert specializing in AI interpretation and causality; uncover ML model causality relationships; build frameworks to measure model bias, underspecification, and latent drivers and their connections. Create an enterprise domain-specific reasoning system to boost actionable insights and optimize machine learning process resources. Orchestrate reusable storytelling methodology to apply toward AI translation. Apply an inquisitive approach to creating ML/AI transparency for the business and translate AI reasoning into actionable business recommendations. Apply AI research to accelerate business innovation. What is your background?
A related degree or comparable formal training, certification, or work experience. 7+ 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. Required capabilities
Technical knowledge in programming languages: SQL, R, Python, Scala, Java, C/C++. Technical knowledge in big data / ML optimization: GPU code optimization, Horovod, Spark MLlib optimization, Cython, JNI, Numba. Technical knowledge in mainstream ML/AI: manifold learning, distributed clustering, graph networks, hierarchical models, Bayesian networks, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-learning, federated learning. Ability to consider and apply causal reasoning representation and learning, and human-centric, explainable, responsible AI. Ability to translate business questions into ML solutions and tell a compelling data story. Ability to work with imperfect or incomplete data and apply AI reasoning to business action recommendations. Additional expectations
Work in a fast-paced retail environment with frequently shifting priorities. Be willing to work extended hours; sit for long periods. Organizational Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Retail
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Join to apply for the
Sr Data Scientist - ML Engineering
role at
H-E-B Join to apply for the
Sr Data Scientist - ML Engineering
role at
H-E-B Responsibilities
Design, create, and maintain an ML platform and related environments. Manage Docker containers and Kubernetes clusters, oversee dependencies and configurations, and implement CI/CD pipelines for automated building, testing, and deployment of machine learning models. Monitor and optimize model training performance and resource usage. Deploy ML models to production environments and manage model versioning and rollback mechanisms. Ensure scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet growing demands as the number and complexity of ML models increase. Work closely with data scientists, data engineers, and stakeholders to understand and fulfill infrastructure needs; stay updated with the latest ML infrastructure technologies and best practices. Architect and develop Generative AI solutions utilizing ML and GenAI techniques; collaborate with leadership to identify AI opportunities and promote AI strategy; specialize in engineering and deploying Generative AI models, with a focus on Retrieval-Augmented Generation (RAG) systems, search, knowledge graphs, and multi-agent workflows. Handle both unstructured and structured data, preparing it to be used as context for Language Model Learning (LLM); tasks include embedding large text corpora, developing generative SQL queries, and building connectors to structured databases. Train models on prepared data and fine-tune hyperparameters to achieve optimal performance. Analytics / Design & Development
Build a framework to stitch cross-domain learning and optimize them toward mission-specific and multi-mission tasks. Serve as an expert specializing in AI interpretation and causality; uncover ML model causality relationships; build frameworks to measure model bias, underspecification, and latent drivers and their connections. Create an enterprise domain-specific reasoning system to boost actionable insights and optimize machine learning process resources. Orchestrate reusable storytelling methodology to apply toward AI translation. Apply an inquisitive approach to creating ML/AI transparency for the business and translate AI reasoning into actionable business recommendations. Apply AI research to accelerate business innovation. What is your background?
A related degree or comparable formal training, certification, or work experience. 7+ 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. Required capabilities
Technical knowledge in programming languages: SQL, R, Python, Scala, Java, C/C++. Technical knowledge in big data / ML optimization: GPU code optimization, Horovod, Spark MLlib optimization, Cython, JNI, Numba. Technical knowledge in mainstream ML/AI: manifold learning, distributed clustering, graph networks, hierarchical models, Bayesian networks, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-learning, federated learning. Ability to consider and apply causal reasoning representation and learning, and human-centric, explainable, responsible AI. Ability to translate business questions into ML solutions and tell a compelling data story. Ability to work with imperfect or incomplete data and apply AI reasoning to business action recommendations. Additional expectations
Work in a fast-paced retail environment with frequently shifting priorities. Be willing to work extended hours; sit for long periods. Organizational Details
Seniority level: Mid-Senior level Employment type: Full-time Job function: Engineering and Information Technology Industries: Retail
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