Data Scientist - ML Engineering

About the Role

H-E-B's Corporate Planning and Analysis Team develops and maintains budgets and financial systems while providing current, reliable financial data, analysis, and technical information. As a Senior Data Scientist, you will act as a Business Decision Scientist, building frameworks to stitch cross-domain learning, uncovering ML model causality relationships, and creating enterprise domain-specific reasoning systems to boost actionable insights. You will also orchestrate reusable storytelling methodologies for AI translation. Once eligible, you'll become an Owner in the company, contributing to innovation, growth, and success.

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.
• Work closely with data scientists, data engineers, and stakeholders to understand and fulfill their infrastructure needs.
• Stay updated with the latest technologies and best practices in ML infrastructure.
• Architect and develop Generative AI solutions utilizing Machine Learning 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 for use as context for Language Model Learning (LLM).
• Train models on prepared data and fine-tune hyperparameters to achieve optimal performance.
• Build a framework to stitch cross-domain learning and optimize toward mission-specific and multi-mission tasks.
• Serve as an expert in AI interpretation and causality; uncover ML model causality relationships.
• Create a framework to measure each model's bias, underspecification, and latent drivers with their connections.
• Develop an enterprise domain-specific reasoning system to boost actionable insights and optimize machine learning resources.
• Orchestrate reusable storytelling methodologies for AI translation.
• Apply an inquisitive nature to create ML/AI transparency for the business.
• Translate AI reasoning into business action recommendations.
• Apply AI research to accelerate business innovation.

Requirements

• 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.
• 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.
• Technical skills to consider and apply causal reasoning representation and learning, and human-centric, explainable, responsible AI.
• Ability as a creative storyteller and translator between business questions and ML solutions.
• Ability to work comfortably with imperfect or incomplete data.
• Ability to apply AI reasoning into business action recommendations.
• Willingness to mentor and collaborate cross-functionally.
• Ability to work in a fast-paced retail environment with frequently shifting priorities.
• Ability to work extended hours and sit for long periods.

Traits We Value

• HEART FOR PEOPLE: Willingness to mentor and support team members.
• HEAD FOR BUSINESS: Skills to serve as a technical lead and support decision-making for complex cross-functional business issues.
• PASSION FOR RESULTS: Ability to generate business-valued questions and data-driven solutions.