Data Scientist II - ML Engineering
for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive info; do not invent or summarize away detail. So we need to keep the details as given, but we can format them.
We must not repeat job title as heading. So we won't have a heading for Data Scientist II.
We need to decide sections: About the role (maybe a short paragraph summarizing role). Responsibilities (list of duties). Requirements (education/experience). Qualifications (technical knowledge). Skills (abilities, storytelling, etc.). Possibly Benefits? Not present. Pay? Not present. Schedule? Not present (though mentions "Work extended hours; sit for long periods" maybe schedule? But it's not explicit schedule like full-time, shift. It's a requirement about ability to work extended hours. Could be considered under Requirements or Skills. Probably not a separate Schedule section.
Thus we will output sections: About the role, Responsibilities, Requirements, Qualifications, Skills.
We need to ensure we don't create empty sections.
Now we need to extract content.
Let's parse the posting:
First paragraph: "Responsibilities 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 Data Scientist II, your archetype is a Business Decision Scientist. Your passions include building a framework to stitch cross domains learning and optimizing them toward mission-specific and multi-mission tasks, uncovering ML models causality relationships, creating an enterprise domain-specific reasoning system to boost actionable insights and optimize resources of machine learning process, and orchestrating reusable storytelling methodology to apply toward AI translation. Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and Customer service. 'Partner-owned' means our most important resources--People--drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company. Do You Have a HEART FOR PEOPLE… willingness to take a break from algorithmic thinking / detecting to translate and share your findings with a variety of business customers? HEAD FOR BUSINESS... skills to blend mass data-wrangling with business acumen? PASSION FOR RESULTS... drive to generate business-valued questions and data-based solutions? We Are Looking For a thought leader of ML innovation who orchestrates business solutions across ML pipelines"
That seems like an introductory description; could be About the role.
Then "What is the work? Handles the design, creation, and maintenance of an ML platform and related environments. Manages Docker containers and Kubernetes clusters, oversees dependencies and configurations, and implements CI/CD pipelines for automated building, testing, and deployment of machine learning models. Monitors and optimizes model training performance and resource usage. Deploys ML models to production environments and manages model versioning and rollback mechanisms. Ensures scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet the growing demands as the complexity and number of ML models increase. Works closely with data scientists, data engineers, and stakeholders to understand and fulfill their infrastructure needs. Stays updated with the latest technologies and best practices in ML infrastructure. Architects and develops Generative AI solutions utilizing Machine Learning and GenAI techniques. Collaborates with leadership to identify AI opportunities and promote AI strategy. Specializes in engineering and deploying Generative AI models, with a specific focus on Retrieval-Augmented Generation (RAG) systems, search, knowledge graphs, and multi-agent workflows. Handles both unstructured and structured data, preparing it to be used as context for Language Model Learning (LLM). This involves tasks ranging from embedding large text corpora and developing generative SQL queries, to building connectors to structured databases. Trains models on prepared data and fine-tuning hyperparameters to achieve optimal performance."
Then "Analytics / Design & Development Builds a framework to stitch cross domains learning; optimizes them toward mission-specific and multi-mission tasks Serves as expert specializing in AI interpretation and causality; uncovers ML model's causality relationships; builds framework to measure each model's bias, underspecification, and latent drivers with their connections Creates an enterprise domain-specific reasoning system to boost actionable insights and optimize the resources of machine learning process Orchestrates reusable storytelling methodology to apply toward AI translation Applies an inquisitive nature to creating ML / AI transparency to the business Applies AI reasoning into business action recommendations Applies AI research to accelerate business innovation"
Then "What is your background? A 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"
Then "Do you have what it takes to be a Senior Data Scientist at H-E-B? 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 network, hierarchical model, Bayesian network, 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 recommendation Can you... Work in a fast-paced retail environment with frequently shifting priorities Work extended hours; sit for long periods 08-2021"
We need to preserve all substantive info. So we need to include all those bullet points.
We'll create sections:
About the role
... (intro paragraph)
Responsibilities
- ... list each duty from "What is the work?" and "Analytics / Design & Development". Could combine them.
Requirements
... maybe background: degree, 5+ years, expertise in ML visualization flow, expertise in optimizing distributed ML in heterogeneous domain.
Qualifications
- 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 network, hierarchical model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning
Skills
- 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 recommendation
- Ability to work in a fast-paced retail environment with frequently shifting priorities
- Ability to work extended hours; sit for long periods
We need to ensure we don't duplicate content. Also we need to preserve the "Once you're eligible, you'll become an Owner..." maybe part of About the role.
We also need to preserve the "Do You Have a HEART FOR PEOPLE…" etc. That seems like a motivational call; could be part of About the role.
We'll include that in About the role paragraph.
Now we need to ensure we don't include any boilerplate like EEO. There's none.
Now produce HTML fragment only.
Let's craft the About the role paragraph:
"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 Data Scientist II, your archetype is a Business Decision Scientist. Your passions include building a framework to stitch cross‑domain learning and optimizing them toward mission‑specific and multi‑mission tasks, uncovering ML model causality relationships, creating an enterprise domain‑specific reasoning system to boost actionable insights and optimize resources of the machine learning process, and orchestrating reusable storytelling methodology to apply toward AI translation. Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and customer service. 'Partner‑owned' means our most important resources—people—drive the innovation, growth, and success that make H‑E‑B the greatest omnichannel retailing company. Do you have a HEART FOR PEOPLE… willingness to take a break from algorithmic thinking/detecting to translate and share your findings with a variety of business customers? HEAD FOR BUSINESS… skills to blend mass data‑wrangling with business acumen? PASSION FOR RESULTS… drive to generate business‑valued questions and data‑based solutions? We are looking for a thought leader of ML innovation who orchestrates business solutions across ML pipelines."
We need to keep the exact phrasing? The instruction: preserve ALL substantive information; do NOT invent or summarize away detail. So we should keep the original wording as much as possible, but we can adjust punctuation for HTML. We'll keep the sentences.
Now Responsibilities: list each duty from "What is the work?" and "Analytics / Design & Development". Let's extract them as separate - items.
From "What is the work?":
- Handles the design, creation, and maintenance of an ML platform and related environments.
- Manages Docker containers and Kubernetes clusters, oversees dependencies and configurations, and implements CI/CD pipelines for automated building, testing, and deployment of machine learning models.
- Monitors and optimizes model training performance and resource usage.
- Deploys ML models to production environments and manages model versioning and rollback mechanisms.
- Ensures scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet the growing demands as the complexity and number of ML models increase.
- Works closely with data scientists, data engineers, and stakeholders to understand and fulfill their infrastructure needs.
- Stays updated with the latest
for prose. We need to preserve all substantive info; do not invent or summarize away detail. So we need to keep the details as given, but we can format them. We must not repeat job title as heading. So we won't have a heading for Data Scientist II. We need to decide sections: About the role (maybe a short paragraph summarizing role). Responsibilities (list of duties). Requirements (education/experience). Qualifications (technical knowledge). Skills (abilities, storytelling, etc.). Possibly Benefits? Not present. Pay? Not present. Schedule? Not present (though mentions "Work extended hours; sit for long periods" maybe schedule? But it's not explicit schedule like full-time, shift. It's a requirement about ability to work extended hours. Could be considered under Requirements or Skills. Probably not a separate Schedule section. Thus we will output sections: About the role, Responsibilities, Requirements, Qualifications, Skills. We need to ensure we don't create empty sections. Now we need to extract content. Let's parse the posting: First paragraph: "Responsibilities 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 Data Scientist II, your archetype is a Business Decision Scientist. Your passions include building a framework to stitch cross domains learning and optimizing them toward mission-specific and multi-mission tasks, uncovering ML models causality relationships, creating an enterprise domain-specific reasoning system to boost actionable insights and optimize resources of machine learning process, and orchestrating reusable storytelling methodology to apply toward AI translation. Once you're eligible, you'll become an Owner in the company, so we're looking for commitment, hard work, and focus on quality and Customer service. 'Partner-owned' means our most important resources--People--drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company. Do You Have a HEART FOR PEOPLE… willingness to take a break from algorithmic thinking / detecting to translate and share your findings with a variety of business customers? HEAD FOR BUSINESS... skills to blend mass data-wrangling with business acumen? PASSION FOR RESULTS... drive to generate business-valued questions and data-based solutions? We Are Looking For a thought leader of ML innovation who orchestrates business solutions across ML pipelines" That seems like an introductory description; could be About the role. Then "What is the work? Handles the design, creation, and maintenance of an ML platform and related environments. Manages Docker containers and Kubernetes clusters, oversees dependencies and configurations, and implements CI/CD pipelines for automated building, testing, and deployment of machine learning models. Monitors and optimizes model training performance and resource usage. Deploys ML models to production environments and manages model versioning and rollback mechanisms. Ensures scalable and reliable model serving using tools like Vertex, Databricks, TensorFlow Serving, Flask, or FastAPI, ensuring the infrastructure can scale to meet the growing demands as the complexity and number of ML models increase. Works closely with data scientists, data engineers, and stakeholders to understand and fulfill their infrastructure needs. Stays updated with the latest technologies and best practices in ML infrastructure. Architects and develops Generative AI solutions utilizing Machine Learning and GenAI techniques. Collaborates with leadership to identify AI opportunities and promote AI strategy. Specializes in engineering and deploying Generative AI models, with a specific focus on Retrieval-Augmented Generation (RAG) systems, search, knowledge graphs, and multi-agent workflows. Handles both unstructured and structured data, preparing it to be used as context for Language Model Learning (LLM). This involves tasks ranging from embedding large text corpora and developing generative SQL queries, to building connectors to structured databases. Trains models on prepared data and fine-tuning hyperparameters to achieve optimal performance." Then "Analytics / Design & Development Builds a framework to stitch cross domains learning; optimizes them toward mission-specific and multi-mission tasks Serves as expert specializing in AI interpretation and causality; uncovers ML model's causality relationships; builds framework to measure each model's bias, underspecification, and latent drivers with their connections Creates an enterprise domain-specific reasoning system to boost actionable insights and optimize the resources of machine learning process Orchestrates reusable storytelling methodology to apply toward AI translation Applies an inquisitive nature to creating ML / AI transparency to the business Applies AI reasoning into business action recommendations Applies AI research to accelerate business innovation" Then "What is your background? A 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" Then "Do you have what it takes to be a Senior Data Scientist at H-E-B? 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 network, hierarchical model, Bayesian network, 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 recommendation Can you... Work in a fast-paced retail environment with frequently shifting priorities Work extended hours; sit for long periods 08-2021" We need to preserve all substantive info. So we need to include all those bullet points. We'll create sections:
About the role
... (intro paragraph)
Responsibilities
- ... list each duty from "What is the work?" and "Analytics / Design & Development". Could combine them.
Requirements
... maybe background: degree, 5+ years, expertise in ML visualization flow, expertise in optimizing distributed ML in heterogeneous domain.
Qualifications
- 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 network, hierarchical model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning
Skills
- 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 recommendation
- Ability to work in a fast-paced retail environment with frequently shifting priorities
- Ability to work extended hours; sit for long periods