Senior Specialist, AI Model Validation
for section headings. Use - for any list of duties/requirements/benefits, and
for prose.
We must preserve all substantive info, not invent.
We need to include sections only when posting has real specific content.
Let's parse the posting.
First paragraph: "Your opportunity At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI /ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models. The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders. Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship."
This is basically description of role, maybe "About the role". Could be "About the role" section.
Next: "What you will do" bullet list (actually sentences). This is responsibilities.
Then "What you will learn" bullet list (learning outcomes). Could be part of responsibilities or maybe "What you will learn" but not a required section; we could include under Responsibilities or maybe a separate "Learning Opportunities"? Not in allowed sections. Could treat as part of Responsibilities or maybe "What you will learn" as part of Responsibilities? But we have only allowed sections; we could put under Responsibilities as prose.
Then "What you have" with Required Qualifications and Preferred Qualifications. That's qualifications.
Then "How you will succeed" maybe part of Skills? Could be "Skills" or "Qualifications". Might be considered "Skills". But we can include as part of Qualifications or Skills.
Then "Role details" includes career level, team, role type, primary focus. That could be "About the role" maybe.
Then "What’s in it for you" includes benefits description. That's Benefits.
We also have schedule info: "Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home)." That's schedule.
Pay: Not mentioned. So we should NOT include Pay section.
Benefits: described: "We offer a competitive benefits package that takes care of the whole you – both today and in the future: 401(k) with company match and Employee stock purchase plan Paid time for vacation, volunteering, and 28-day sabbatical after every 5 years of service for eligible positions Paid parental leave and family building benefits Tuition reimbursement Health, dental, and vision insurance". That's benefits.
We need to output sections only when content present.
Potential sections:
- About the role (could combine initial paragraph and role details)
- Responsibilities (What you will do + What you will learn)
- Requirements (maybe Required Qualifications)
- Qualifications (maybe Preferred Qualifications) but we have both Required and Preferred; we could combine into Requirements and Qualifications? The rules list both Requirements and Qualifications as possible sections. We could put Required Qualifications under Requirements, Preferred under Qualifications.
- Skills (maybe "How you will succeed" or technical skills list). Could also include proficiency in Python etc. That's under Required Qualifications. Might be better to put skills under Skills section.
- Benefits (as described)
- Schedule (the hybrid work model)
We must not invent content.
Let's extract specifics.
About the role: We'll combine the introductory paragraph and role details.
Intro paragraph: "Your opportunity At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI /ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models. The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders."
Role details: "Career level: Senior Specialist, Risk Analytics/Modeling Team: Model Risk Oversight, Artificial Intelligence Validation Role type: Individual contributor Primary focus: AI/ML and generative AI model validation, effective challenge, and ongoing model oversight"
We can combine.
Responsibilities: "What you will do" list:
- Validate AI and machine learning models by independently assessing intended use, design choices, data, implementation, controls, performance, and limitations.
- Build reproducible testing frameworks, analyses, and prototypes to evaluate model quality, robustness, safety, and reliability.
- Evaluate generative AI and retrieval-augmented generation applications, including prompt behavior, retrieval quality, groundedness, hallucination risk, response consistency, and guardrail effectiveness.
- The candidate will apply statistical and machine learning methods to benchmark models, analyze errors, compare alternatives, and assess results.
- Review source code, notebooks, technical documentation, model inventories, performance monitoring, and production-control evidence.
- Partner with model developers, product teams, data scientists, engineers, risk partners, and external consultants to understand systems and challenge assumptions constructively.
- Document findings in clear validation reports, technical appendices, issue statements, and executive-ready presentations.
- Evaluate ongoing performance monitoring and participate in periodic reviews of models after implementation.
- Contribute to reusable validation frameworks, testing utilities, and effective-challenge methods as AI capabilities and industry practices evolve.
Also "What you will learn" could be considered responsibilities or learning outcomes. We could include as part of Responsibilities or maybe separate but not needed. We'll include as part of Responsibilities as prose or bullet list? It's a list of learning outcomes. Could be under Responsibilities as bullet list.
What you will learn:
- How to design production AI systems, testing, governance, and monitoring in a large financial institution.
- How to translate open-ended model risk questions into structured experiments and defensible conclusions.
- How to communicate technical findings to audiences ranging from ML engineers to senior management.
- How responsible AI, model risk management, software controls, and regulatory expectations come together in practice.
We could treat as responsibilities (what you will do/learn). We'll include as bullet list under Responsibilities.
Requirements: Required Qualifications:
- Master's degree, or bachelor's degree with strong relevant project or research experience, in computer science, data science, statistics, applied mathematics, engineering, economics, quantitative finance, or a related field.
- 0-2 years of professional experience. Relevant internships, research, capstone projects, open-source contributions, or substantial independent projects are welcome.
- Proficiency in Python and experience using common data science or machine learning libraries.
- Working knowledge of core machine learning concepts, experimental design, model evaluation, and statistical reasoning.
- Ability to read technical documentation and code, investigate unfamiliar systems, and communicate conclusions clearly.
- Curiosity, sound judgment, attention to detail, and a willingness to challenge assumptions with evidence.
Qualifications: Preferred Qualifications:
- Hands-on experience with large language models, generative AI, natural language processing, embeddings, vector search, or retrieval-augmented generation.
- Experience with evaluation frameworks, prompt testing, red teaming, model monitoring, or responsible AI techniques.
- Familiarity with SQL, Git, containers, cloud platforms, or modern ML development workflows.
- Understanding software testing, data quality, reproducibility, or model deployment concepts.
- Experience presenting technical work through research papers, project reports, demos, or presentations.
- Interest in financial services, model risk management, AI governance, or building trustworthy AI systems.
Skills: Could be "How you will succeed" paragraph: "You do not need to arrive as an expert in financial regulation or model validation. Successful candidates bring strong technical fundamentals, intellectual curiosity, disciplined problem solving, and the ability to explain complex work clearly. We value thoughtful individuals who enjoy learning quickly, testing ideas, finding weaknesses before they become problems, and improving evaluation of AI systems."
That's more about success traits; could be under Skills or maybe under Qualifications. We'll put under Skills as prose.
Benefits: as described.
Schedule: "Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship."
We need to include schedule: M-F standard business hours, hybrid work model (4 days in-office, 1 day WFH). Also location restriction and work authorization. Those are schedule/location details. We'll include schedule section with that info.
We must not include location list because not provided; we can mention "only available in the areas listed" but we don't have the list; we can keep that phrase as is.
We must not include boilerplate like EEO etc. Not present.
Now produce HTML.
Structure:
About the role
...combined intro and role details...
Responsibilities
- ... each bullet from What you will do ...
- ... etc ...
What you will learn:
- ... each bullet ...
Alternatively combine all bullets under one ul; but we need to preserve all info. Could separate.
Better: Under Responsibilities, we can have two paragraphs: one for duties, one for learning. But rule: Use - for any list of duties/requirements/benefits. So we need to use ul for lists. For prose we use p.
Thus we can have a p for intro sentence then ul for duties, then p for learning intro then ul for learning.
But we can also combine duties and learning into one ul if we want; but better separate.
Similarly for Requirements and Qualifications.
Requirements: Use ul for each required qualification.
Qualifications: Use ul for each preferred qualification.
Skills: Use p for the "How you will succeed" paragraph.
Benefits: Use ul for each benefit item.
Schedule: Use p for schedule info.
Let's craft.
About the role paragraph: combine:
"Your opportunity At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI/ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models. The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders.
Career level: Senior Specialist, Risk Analytics/Modeling. Team: Model Risk Oversight, Artificial Intelligence Validation. Role type: Individual contributor. Primary focus: AI/ML and generative AI model validation, effective challenge, and ongoing model oversight
for prose. We must preserve all substantive info, not invent. We need to include sections only when posting has real specific content. Let's parse the posting. First paragraph: "Your opportunity At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI /ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models. The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders. Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship." This is basically description of role, maybe "About the role". Could be "About the role" section. Next: "What you will do" bullet list (actually sentences). This is responsibilities. Then "What you will learn" bullet list (learning outcomes). Could be part of responsibilities or maybe "What you will learn" but not a required section; we could include under Responsibilities or maybe a separate "Learning Opportunities"? Not in allowed sections. Could treat as part of Responsibilities or maybe "What you will learn" as part of Responsibilities? But we have only allowed sections; we could put under Responsibilities as prose. Then "What you have" with Required Qualifications and Preferred Qualifications. That's qualifications. Then "How you will succeed" maybe part of Skills? Could be "Skills" or "Qualifications". Might be considered "Skills". But we can include as part of Qualifications or Skills. Then "Role details" includes career level, team, role type, primary focus. That could be "About the role" maybe. Then "What’s in it for you" includes benefits description. That's Benefits. We also have schedule info: "Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home)." That's schedule. Pay: Not mentioned. So we should NOT include Pay section. Benefits: described: "We offer a competitive benefits package that takes care of the whole you – both today and in the future: 401(k) with company match and Employee stock purchase plan Paid time for vacation, volunteering, and 28-day sabbatical after every 5 years of service for eligible positions Paid parental leave and family building benefits Tuition reimbursement Health, dental, and vision insurance". That's benefits. We need to output sections only when content present. Potential sections: - About the role (could combine initial paragraph and role details) - Responsibilities (What you will do + What you will learn) - Requirements (maybe Required Qualifications) - Qualifications (maybe Preferred Qualifications) but we have both Required and Preferred; we could combine into Requirements and Qualifications? The rules list both Requirements and Qualifications as possible sections. We could put Required Qualifications under Requirements, Preferred under Qualifications. - Skills (maybe "How you will succeed" or technical skills list). Could also include proficiency in Python etc. That's under Required Qualifications. Might be better to put skills under Skills section. - Benefits (as described) - Schedule (the hybrid work model) We must not invent content. Let's extract specifics. About the role: We'll combine the introductory paragraph and role details. Intro paragraph: "Your opportunity At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI /ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models. The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders." Role details: "Career level: Senior Specialist, Risk Analytics/Modeling Team: Model Risk Oversight, Artificial Intelligence Validation Role type: Individual contributor Primary focus: AI/ML and generative AI model validation, effective challenge, and ongoing model oversight" We can combine. Responsibilities: "What you will do" list: - Validate AI and machine learning models by independently assessing intended use, design choices, data, implementation, controls, performance, and limitations. - Build reproducible testing frameworks, analyses, and prototypes to evaluate model quality, robustness, safety, and reliability. - Evaluate generative AI and retrieval-augmented generation applications, including prompt behavior, retrieval quality, groundedness, hallucination risk, response consistency, and guardrail effectiveness. - The candidate will apply statistical and machine learning methods to benchmark models, analyze errors, compare alternatives, and assess results. - Review source code, notebooks, technical documentation, model inventories, performance monitoring, and production-control evidence. - Partner with model developers, product teams, data scientists, engineers, risk partners, and external consultants to understand systems and challenge assumptions constructively. - Document findings in clear validation reports, technical appendices, issue statements, and executive-ready presentations. - Evaluate ongoing performance monitoring and participate in periodic reviews of models after implementation. - Contribute to reusable validation frameworks, testing utilities, and effective-challenge methods as AI capabilities and industry practices evolve. Also "What you will learn" could be considered responsibilities or learning outcomes. We could include as part of Responsibilities or maybe separate but not needed. We'll include as part of Responsibilities as prose or bullet list? It's a list of learning outcomes. Could be under Responsibilities as bullet list. What you will learn: - How to design production AI systems, testing, governance, and monitoring in a large financial institution. - How to translate open-ended model risk questions into structured experiments and defensible conclusions. - How to communicate technical findings to audiences ranging from ML engineers to senior management. - How responsible AI, model risk management, software controls, and regulatory expectations come together in practice. We could treat as responsibilities (what you will do/learn). We'll include as bullet list under Responsibilities. Requirements: Required Qualifications: - Master's degree, or bachelor's degree with strong relevant project or research experience, in computer science, data science, statistics, applied mathematics, engineering, economics, quantitative finance, or a related field. - 0-2 years of professional experience. Relevant internships, research, capstone projects, open-source contributions, or substantial independent projects are welcome. - Proficiency in Python and experience using common data science or machine learning libraries. - Working knowledge of core machine learning concepts, experimental design, model evaluation, and statistical reasoning. - Ability to read technical documentation and code, investigate unfamiliar systems, and communicate conclusions clearly. - Curiosity, sound judgment, attention to detail, and a willingness to challenge assumptions with evidence. Qualifications: Preferred Qualifications: - Hands-on experience with large language models, generative AI, natural language processing, embeddings, vector search, or retrieval-augmented generation. - Experience with evaluation frameworks, prompt testing, red teaming, model monitoring, or responsible AI techniques. - Familiarity with SQL, Git, containers, cloud platforms, or modern ML development workflows. - Understanding software testing, data quality, reproducibility, or model deployment concepts. - Experience presenting technical work through research papers, project reports, demos, or presentations. - Interest in financial services, model risk management, AI governance, or building trustworthy AI systems. Skills: Could be "How you will succeed" paragraph: "You do not need to arrive as an expert in financial regulation or model validation. Successful candidates bring strong technical fundamentals, intellectual curiosity, disciplined problem solving, and the ability to explain complex work clearly. We value thoughtful individuals who enjoy learning quickly, testing ideas, finding weaknesses before they become problems, and improving evaluation of AI systems." That's more about success traits; could be under Skills or maybe under Qualifications. We'll put under Skills as prose. Benefits: as described. Schedule: "Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship." We need to include schedule: M-F standard business hours, hybrid work model (4 days in-office, 1 day WFH). Also location restriction and work authorization. Those are schedule/location details. We'll include schedule section with that info. We must not include location list because not provided; we can mention "only available in the areas listed" but we don't have the list; we can keep that phrase as is. We must not include boilerplate like EEO etc. Not present. Now produce HTML. Structure:
About the role
...combined intro and role details...
Responsibilities
- ... each bullet from What you will do ...
- ... etc ...
What you will learn:
- ... each bullet ...
- for any list of duties/requirements/benefits. So we need to use ul for lists. For prose we use p. Thus we can have a p for intro sentence then ul for duties, then p for learning intro then ul for learning. But we can also combine duties and learning into one ul if we want; but better separate. Similarly for Requirements and Qualifications. Requirements: Use ul for each required qualification. Qualifications: Use ul for each preferred qualification. Skills: Use p for the "How you will succeed" paragraph. Benefits: Use ul for each benefit item. Schedule: Use p for schedule info. Let's craft. About the role paragraph: combine: "Your opportunity At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI/ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models. The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders. Career level: Senior Specialist, Risk Analytics/Modeling. Team: Model Risk Oversight, Artificial Intelligence Validation. Role type: Individual contributor. Primary focus: AI/ML and generative AI model validation, effective challenge, and ongoing model oversight