Data Science Chief Expert, Finance
SAP · Palo Alto, CA · 1 mo ago
HybridFinanceFull-time
We need to output HTML fragment with sections with headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have About the role (intro). Then Responsibilities (What You'll Build). Then Requirements (Required Qualifications). Then Qualifications (Preferred Qualification). Then Skills (maybe list of technical skills). Then Benefits (mention great benefits and link). Then Pay (range). No Schedule.
We must not repeat job title. Not include Job ID etc.
We need to preserve all substantive information; do not invent or summarize away detail. So we need to include details from each section.
Let's extract content.
About the role: The introductory paragraphs up to "What You'll Build". Let's capture:
"We help the world run better At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. The context engine that makes AI enterprise ready. Anyone can build an AI agent. What makes SAP's agents different is accuracy grounded in the richest enterprise data and process context in the world. As part of our Data and Applied Science team, you'll build the context engine grounded in SAP’s Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAP's AI agents and assistants."
That seems like About the role.
Responsibilities: "What You'll Build" bullet points (the paragraph after that). Let's capture the paragraph:
"The Data and Applied Science team will build the semantic and contextual foundation of SAP's AI. While generic AI agents operate on surface-level patterns, SAP agents are accurate because they understand the real semantics of enterprise business master data, process flows, and domain relationships. You will help build and scale the layer that makes that possible. This may include the following:"
Then list items (starting with "Leverage deep SAP data...").
We need to preserve list items as
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Must not repeat job title. No Job ID etc. Remove boilerplate. Must preserve all substantive info. We need to parse the posting and extract sections. The posting includes: - Intro: "We help the world run better At SAP..." etc. That's about the company and role description. - "What You'll Build" section: description of responsibilities. - "What You'll Bring" includes Required Qualifications and Preferred Qualification. - "Where You Belong" maybe about team/culture. - "Bring out your best SAP innovations..." more company description. - "At SAP, you can bring out your best." etc. - "We win with inclusion" etc. - "Compensation Range Transparency:" includes pay range. - Benefits: mention "Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed." Also later "Please reference this link for a summary of SAP benefits and eligibility requirements: SAP North America Benefits." So benefits mentioned but not detailed. We have some benefits statement but not specific list. The rule: only include Benefits section if posting actually states them. It does mention "great benefits" and link but not specific list. Might be considered substantive? It says "great benefits" but not list. Might be too vague. The instruction: never emit a heading followed by filler/placeholder text (e.g., "commensurate with experience", "not specified", "N/A", "varies", "TBD") and never create Pay/Schedule/Benefits sections unless the posting actually states them. It does state "great benefits" and a link. That's somewhat substantive but not a list. Could we include Benefits with a
describing that? Probably okay. Pay: they give compensation range: 274300-609200 USD. So we can include Pay section. Schedule: not mentioned (no schedule). So omit. Skills: we could extract skills from qualifications: Python, SQL, PyTorch, TensorFlow, scikit-learn, etc. Also experience with Databricks, AWS, Azure, GCP, SAP Datasphere, HANA Cloud, etc. Also ontologies OWL, RDF/RDFS, SKOS, SHACL, SPARQL, Cypher, GQL. Also knowledge of SAP data models, etc. So we can create Skills section. Requirements: maybe combine Required Qualifications and Preferred Qualification? The instruction: Use headings for Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which sections to include. We have Responsibilities (What You'll Build). We have Requirements (maybe Required Qualifications). We have Qualifications (maybe Preferred Qualification). Or we could combine Required and Preferred under Qualifications. But we have both headings available. Let's include: -
About the role
maybe? The instruction says use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have About the role (intro). Then Responsibilities (What You'll Build). Then Requirements (Required Qualifications). Then Qualifications (Preferred Qualification). Then Skills (maybe list of technical skills). Then Benefits (mention great benefits and link). Then Pay (range). No Schedule.
We must not repeat job title. Not include Job ID etc.
We need to preserve all substantive information; do not invent or summarize away detail. So we need to include details from each section.
Let's extract content.
About the role: The introductory paragraphs up to "What You'll Build". Let's capture:
"We help the world run better At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. The context engine that makes AI enterprise ready. Anyone can build an AI agent. What makes SAP's agents different is accuracy grounded in the richest enterprise data and process context in the world. As part of our Data and Applied Science team, you'll build the context engine grounded in SAP’s Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAP's AI agents and assistants."
That seems like About the role.
Responsibilities: "What You'll Build" bullet points (the paragraph after that). Let's capture the paragraph:
"The Data and Applied Science team will build the semantic and contextual foundation of SAP's AI. While generic AI agents operate on surface-level patterns, SAP agents are accurate because they understand the real semantics of enterprise business master data, process flows, and domain relationships. You will help build and scale the layer that makes that possible. This may include the following:"
Then list items (starting with "Leverage deep SAP data...").
We need to preserve list items as - . So we will create a
with each bullet as - .
Let's extract each bullet:
1. Leverage deep SAP data and process understanding — including SAP data models, metadata structures, and end-to-end business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce — to build AI and semantic data solutions using SAP master data domains, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub assets.
2. Design and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data landscapes, harmonizing sources such as Salesforce, Workday, ServiceNow, MES/IoT systems, and external data providers into unified semantic or analytical layers.
3. Work with cloud and data platforms such as Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, or Google Cloud Platform to support reliable AI workflows.
4. Translate ambiguous business challenges into concrete AI use cases, technical designs, and measurable business outcomes.
5. Design, develop, evaluate, and operationalize end-to-end machine learning and AI solutions — from data preprocessing, feature engineering, experimentation, and validation through to deployment, production handoff, lifecycle support, and continuous improvement.
6. Apply advanced methods across machine learning, deep learning, statistical modeling, data mining, optimization, and applied AI to solve enterprise-scale problems.
7. Develop AI capabilities — including generative AI and LLM-based solutions — using enterprise business data, knowledge graphs, business process intelligence, and other structured and unstructured data assets.
8. Partner closely with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-required.
Note: The last bullet originally: "Partner closely with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-ready." We'll keep that.
Now Requirements (Required Qualifications). Let's extract the list under "What You'll Bring Required Qualifications". It's a paragraph with multiple sentences separated by periods. We need to preserve as list? The instruction: Use
- for any list of duties/requirements/benefits. So we should turn each requirement into a list item.
Let's parse:
- Master's degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative fields.
- 10+ years of experience to include deep expertise in machine learning, deep learning, statistical modeling, generative AI, and LLMs, with hands-on experience developing, evaluating, and improving models using real-world datasets — including data preprocessing, feature engineering, and experimentation — and strong analytical and mathematical modeling skills.
- 10+ years of experience in machine learning, data science, applied AI, AI research, knowledge engineering, or semantic data systems in industry, research labs, or advanced academic environments.
- Strong Python and SQL skills, including production-grade Python development and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
- Demonstrated experience of deploying, shipping, and operating AI or machine learning solutions in production environments, including production handoff and lifecycle support.
- Experience with big data infrastructure, data processing and transformation tools such as Databricks, and cloud environments such as AWS, Azure, or Google Cloud Platform.
- Excellent communication, collaboration, and customer-facing skills, with significant experience in agile development environments and a strong curiosity for exploring new AI techniques and their practical applications for SAP customers and products.
- Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end, including how process semantics map to underlying business objects and datasets.
- Hands-on experience with the SAP data and AI platform stack — including SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub — with working knowledge of SAP master data domains and Master Data Governance constructs.
- Hands-on experience designing and maintaining enterprise ontologies using OWL, RDF/RDFS, SKOS, and SHACL, with proficiency in SPARQL, Cypher, and GQL, and experience evaluating trade-offs between RDF triple stores and labeled property graph databases.
- Experience building entity resolution, deduplication, and identity stitching pipelines across SAP and non-SAP systems, with proven ability to harmonize data into a unified semantic layer using federation, virtualization, replication, and shared ontology mapping approaches.
- Understanding data product and data mesh principles, including semantic contracts and governed self-service consumption.
- Proven experience translating abstract business challenges into concrete AI solutions, delivering from concept through production deployment, production handoff, and business adoption.
- Experience working with cross-functional stakeholders — including product, engineering, business, and customer-facing teams — in agile software development environments and enterprise product organizations.
- Experience building AI capabilities using enterprise business data, knowledge graphs, or business process intelligence.
Now Preferred Qualification: list:
- Experience with Retrieval-Augmented Generation, vector databases, embeddings, semantic retrieval, and enterprise knowledge grounding.
- Experience contributing to reusable AI platforms, foundation model initiatives, shared AI services, or AI capabilities adopted across multiple product areas.
- Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures.
- Ability to design upper-level and mid-level ontologies aligned with industry standards, map application-specific schemas to shared ontologies using declarative mapping standards and apply semantic interoperability frameworks and canonical business entity models across complex application landscapes.
Now Skills: we could extract technical skills from the qualifications, but we already have them in Requirements. However we could still create a Skills section summarizing key technical skills: Python, SQL, PyTorch, TensorFlow, scikit-learn, Databricks, AWS, Azure, GCP, SAP Datasphere, SAP HANA Cloud, SAP One Domain Model, SAP Graph API, SAP Business Accelerator Hub, OWL, RDF/RDFS, SKOS, SHACL, SPARQL, Cypher, GQL, etc. But we risk duplicating content. The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. If we create a Skills section, we need to ensure we are not omitting any info from elsewhere. We could keep the qualifications as is and not create a separate Skills section, to avoid duplication. However the instruction says we can use Skills heading if we have real specific content. We have specific skills listed in the qualifications; we could move them to Skills section and keep Requirements more about experience/education. But we must not lose any info. Let's think: The qualifications list includes both experience and skills. Could split: Requirements: education and years of experience. Skills: technical skills. But the qualifications list includes many items that are both experience and skills. It might be okay to have Requirements include education and experience, and Skills include technical proficiencies. However we need to ensure we don't lose any detail. Let's attempt to
- with each bullet as
- .
Let's extract each bullet:
1. Leverage deep SAP data and process understanding — including SAP data models, metadata structures, and end-to-end business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce — to build AI and semantic data solutions using SAP master data domains, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub assets.
2. Design and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data landscapes, harmonizing sources such as Salesforce, Workday, ServiceNow, MES/IoT systems, and external data providers into unified semantic or analytical layers.
3. Work with cloud and data platforms such as Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, or Google Cloud Platform to support reliable AI workflows.
4. Translate ambiguous business challenges into concrete AI use cases, technical designs, and measurable business outcomes.
5. Design, develop, evaluate, and operationalize end-to-end machine learning and AI solutions — from data preprocessing, feature engineering, experimentation, and validation through to deployment, production handoff, lifecycle support, and continuous improvement.
6. Apply advanced methods across machine learning, deep learning, statistical modeling, data mining, optimization, and applied AI to solve enterprise-scale problems.
7. Develop AI capabilities — including generative AI and LLM-based solutions — using enterprise business data, knowledge graphs, business process intelligence, and other structured and unstructured data assets.
8. Partner closely with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-required.
Note: The last bullet originally: "Partner closely with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-ready." We'll keep that.
Now Requirements (Required Qualifications). Let's extract the list under "What You'll Bring Required Qualifications". It's a paragraph with multiple sentences separated by periods. We need to preserve as list? The instruction: Use
- for any list of duties/requirements/benefits. So we should turn each requirement into a list item. Let's parse: - Master's degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative fields. - 10+ years of experience to include deep expertise in machine learning, deep learning, statistical modeling, generative AI, and LLMs, with hands-on experience developing, evaluating, and improving models using real-world datasets — including data preprocessing, feature engineering, and experimentation — and strong analytical and mathematical modeling skills. - 10+ years of experience in machine learning, data science, applied AI, AI research, knowledge engineering, or semantic data systems in industry, research labs, or advanced academic environments. - Strong Python and SQL skills, including production-grade Python development and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn. - Demonstrated experience of deploying, shipping, and operating AI or machine learning solutions in production environments, including production handoff and lifecycle support. - Experience with big data infrastructure, data processing and transformation tools such as Databricks, and cloud environments such as AWS, Azure, or Google Cloud Platform. - Excellent communication, collaboration, and customer-facing skills, with significant experience in agile development environments and a strong curiosity for exploring new AI techniques and their practical applications for SAP customers and products. - Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end, including how process semantics map to underlying business objects and datasets. - Hands-on experience with the SAP data and AI platform stack — including SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub — with working knowledge of SAP master data domains and Master Data Governance constructs. - Hands-on experience designing and maintaining enterprise ontologies using OWL, RDF/RDFS, SKOS, and SHACL, with proficiency in SPARQL, Cypher, and GQL, and experience evaluating trade-offs between RDF triple stores and labeled property graph databases. - Experience building entity resolution, deduplication, and identity stitching pipelines across SAP and non-SAP systems, with proven ability to harmonize data into a unified semantic layer using federation, virtualization, replication, and shared ontology mapping approaches. - Understanding data product and data mesh principles, including semantic contracts and governed self-service consumption. - Proven experience translating abstract business challenges into concrete AI solutions, delivering from concept through production deployment, production handoff, and business adoption. - Experience working with cross-functional stakeholders — including product, engineering, business, and customer-facing teams — in agile software development environments and enterprise product organizations. - Experience building AI capabilities using enterprise business data, knowledge graphs, or business process intelligence. Now Preferred Qualification: list: - Experience with Retrieval-Augmented Generation, vector databases, embeddings, semantic retrieval, and enterprise knowledge grounding. - Experience contributing to reusable AI platforms, foundation model initiatives, shared AI services, or AI capabilities adopted across multiple product areas. - Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures. - Ability to design upper-level and mid-level ontologies aligned with industry standards, map application-specific schemas to shared ontologies using declarative mapping standards and apply semantic interoperability frameworks and canonical business entity models across complex application landscapes. Now Skills: we could extract technical skills from the qualifications, but we already have them in Requirements. However we could still create a Skills section summarizing key technical skills: Python, SQL, PyTorch, TensorFlow, scikit-learn, Databricks, AWS, Azure, GCP, SAP Datasphere, SAP HANA Cloud, SAP One Domain Model, SAP Graph API, SAP Business Accelerator Hub, OWL, RDF/RDFS, SKOS, SHACL, SPARQL, Cypher, GQL, etc. But we risk duplicating content. The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. If we create a Skills section, we need to ensure we are not omitting any info from elsewhere. We could keep the qualifications as is and not create a separate Skills section, to avoid duplication. However the instruction says we can use Skills heading if we have real specific content. We have specific skills listed in the qualifications; we could move them to Skills section and keep Requirements more about experience/education. But we must not lose any info. Let's think: The qualifications list includes both experience and skills. Could split: Requirements: education and years of experience. Skills: technical skills. But the qualifications list includes many items that are both experience and skills. It might be okay to have Requirements include education and experience, and Skills include technical proficiencies. However we need to ensure we don't lose any detail. Let's attempt to