Applied Scientist, AWS Infrastructure Services Science

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The description includes many paragraphs describing role, responsibilities, qualifications, etc. We need to parse. We need to avoid repeating job title as heading. No Job ID. Remove boilerplate like EEO, accommodation links, etc. Also remove application instructions (none present). Keep substantive info. We need to decide sections: - About the role: likely the introductory paragraph describing AWS Infrastructure Services Science (AISS) and what the role does. Could be a prose. - Responsibilities: list of duties: "Key job responsibilities" bullet list. - Requirements: maybe "Basic Qualifications" list. - Qualifications: maybe "Preferred Qualifications" list. - Skills: maybe not explicit separate; could combine with qualifications? But we can include if there is content about skills: e.g., "Experience programming in Java, C++, Python or related language", "Experience with SQL and an RDBMS", "Experience building machine learning models", etc. Those are qualifications. Could treat as Skills. - Benefits: The paragraph about benefits: "Amazon also offers comprehensive benefits including health insurance ...". Also mention sign-on payments and RSUs. So we have benefits content. - Pay: The base salary range listed: "The base salary range for this position is listed below. ... USA, VA, Arlington - 136,000.00 - 184,000.00 USD annually ...". Also multiple locations. So we have pay info. - Schedule: Not mentioned. No schedule info. So we should not include Schedule section. Thus sections: About the role, Responsibilities, Requirements (Basic Qualifications), Qualifications (Preferred Qualifications), Skills (maybe we can merge with Requirements? But we need to follow instruction: use sections only when posting has real specific content. We have both Basic and Preferred qualifications; we could make Requirements = Basic Qualifications, Qualifications = Preferred Qualifications. Skills maybe not needed separately; but we could still include Skills if we have explicit skill statements not covered elsewhere. However the qualifications already list skills like programming languages, SQL, ML models, etc. Could treat those as Skills section. But we already have Requirements and Qualifications; adding Skills might be redundant but allowed if content distinct. However we should avoid thin sections. The posting does not have a separate "Skills" heading; but we could still create a Skills section if we have content like "You are skeptical..." etc. Those are more about traits/behaviors, not hard skills. Might be better to put those in About the role or Responsibilities. I'd say we omit Skills section to avoid thin. Thus sections: About the role, Responsibilities, Requirements, Qualifications, Benefits, Pay. Now we need to format:

About the role

...

Responsibilities:

Responsibilities

• ... each bullet

Requirements:

Requirements

• ... each bullet from Basic Qualifications

Qualifications:

Qualifications

• ... each bullet from Preferred Qualifications

Benefits:

Benefits

... benefits paragraph

Pay:

Pay

... salary range info

We must preserve all substantive information; not invent or summarize away detail. Need to keep details like "You will be responsible for researching and deploying machine learning models that optimize our power and thermal infrastructure, working across AWS to solve data mapping and quality issues and contribute to our Science team vision." etc. We need to extract the About the role content: The first paragraph up to maybe before "Key job responsibilities". Let's capture: "AWS Infrastructure Services Science (AISS) researches and builds machine learning models that influence the power utilization at our data centers to ensure the health of our thermal and electrical infrastructure at high infrastructure utilization. As an Applied Scientist, you will work on our Science team and partner closely with other scientists, data engineers and Software teams to accurately model and optimize our power infrastructure. Outputs from your models will directly influence our data center topology and will drive exceptional cost savings. You will be responsible for researching and deploying machine learning models that optimize our power and thermal infrastructure, working across AWS to solve data mapping and quality issues and contribute to our Science team vision. You are skeptical. When someone gives you a data source, you pepper them with questions about sampling biases, accuracy, and coverage. When you’re told a model can make assumptions, you proactively try to break those assumptions. You have passion for excellence. The wrong choice of data could cost the business dearly. You maintain rigorous standards and take ownership of the outcome of your data pipelines and code. You raise the bar on the software code standards to delivery faster and efficient solutions. You do whatever it takes to add value. You don’t care whether you’re building complex ML models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and preserve customer availability. You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team. You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to learn Data Center architecture and components of electrical engineering to build your models. You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer availability are constantly up for debate among senior leadership - you will help drive this conversation. This position requires superior analytical thinkers, able to quickly approach large ambiguous problems and apply their technical and statistical knowledge to identify opportunities for further research. You should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon’s strategic needs." That is a long paragraph; we can keep as prose. Now Responsibilities: The "Key job responsibilities" list: - Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems - Disambiguate problems to propose clear evaluation frameworks and success criteria - Work autonomously and write high quality technical documents - Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production - Work closely with other scientists to deliver impactful customer solutions - Share and publish works with the broader scientific community through meetings and conferences - Communicate clearly to scientific audiences We need to keep each as

. Now Requirements (Basic Qualifications): - Master's degree - Experience programming in Java, C++, Python or related language - Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse - Experience building machine learning models or developing algorithms for business application - Experience implementing algorithms using toolkits and self-developed code - Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS Now Qualifications (Preferred Qualifications): - Experience researching about machine learning, deep learning, NLP, computer vision, data science - PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field Now Benefits: Paragraph: "Amazon is an equal opportunity employer ..." but we need to remove EEO boilerplate. The benefits paragraph: "Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits." Also earlier: "Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location." That is part of compensation/benefits. We'll include that in Benefits section. Thus Benefits paragraph: "Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits." We should keep the link? The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The link to benefits is not boilerplate; it's a reference. Might be okay to keep. But it's a URL; we can keep as plain text. Now Pay: The base salary range info: "The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits ...". Actually we already used that in Benefits. The Pay section should contain the salary ranges: "USA, VA, Arlington - 136,000.00 - 184,000.00 USD annually USA, VA, Herndon - 136,000.00 - 184,000.00 USD annually USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually". Also the line "The base salary range for this position is listed below." We can include that as intro. Thus Pay paragraph: "The base salary range for this position is listed below. USA, VA, Arlington - 136,000.00 - 184,000.00 USD annually; USA, VA, Herndon - 136,000.00 - 184,000.00 USD annually; USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually." We should avoid duplication with Benefits; we already mentioned sign-on payments and RSUs in Benefits; but it's okay to repeat? The instruction: preserve ALL substantive information; do not invent or summarize away detail. If we include sign-on payments and RSUs in both Benefits and Pay, that's duplication but not invented. Might be okay but could be considered duplicated lines. Better to keep sign-on payments and RSUs only in Benefits (or Pay). Since Pay section is about salary range, we can keep the sign-on payments and RSUs in Benefits only. The Pay paragraph can just include the base salary range statement and the numbers. Thus Pay: "

The base salary range for this position is listed below. USA, VA, Arlington - 136,000.00 - 184,000.00 USD annually; USA, VA, Herndon - 136,000.00 - 184,000.00 USD annually; USA, WA, Seattle - 136,000.00 - 184,000.00 USD annually.

" Now Benefits paragraph: "Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance