Sr. Applied Scientist, Pricing 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 content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate like EEO, application instructions, etc. We need to parse the description. Let's extract sections: - About the role: The first paragraph after "Description" maybe: "We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment." That's about the role. - Responsibilities: "Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems." Also "A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. Error detection and price quality guardrails at scale. Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication." That could be part of responsibilities or separate. We'll include under Responsibilities. - About The Team: Could be "About The Team" section. Might be considered "About the team" but not required headings list includes "About the role". Not "About the team". Could be part of "About the role" or maybe we treat as "About the team" but not required. The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should only use those headings. So we need to map content to those headings. "About The Team" could be part of "About the role" or maybe we can include as part of "About the role". We'll incorporate team description into About the role. - Basic Qualifications and Preferred Qualifications: Those go under Requirements and Qualifications? The headings list includes Requirements, Qualifications, Skills. We need to decide mapping. Typically "Basic Qualifications" -> Requirements. "Preferred Qualifications" -> Qualifications or Skills. We'll put Basic Qualifications under

Requirements

. Preferred Qualifications under

Qualifications

. Skills maybe we can extract from responsibilities? But we could also put under Skills if we have explicit skill list. There's not a separate skills list; we could omit Skills section if no specific content. The instruction: include a section ONLY when the posting has real, specific content for it. So we may omit Skills if not explicit. - Benefits: The posting mentions benefits: "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." Also mention sign-on payments and RSUs. That's benefits. So we include Benefits section. - Pay: The posting includes salary range: "USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually". Also mention sign-on payments and RSUs. So we include Pay section. - Schedule: Not mentioned. So omit Schedule. We must not include boilerplate like EEO, accommodation link, etc. Remove those. We must preserve all substantive info. Ensure we don't lose details like "4+ years of applied research experience", etc. Let's extract Basic Qualifications: "Basic Qualifications 4+ years of applied research experience 3+ years of building machine learning models for business application experience PhD, or Master's degree and 6+ years of applied research experience Experience programming in Java, C++, Python or related language Experience with neural deep learning methods and machine learning" Preferred Qualifications: "Preferred Qualifications Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. Experience with large scale distributed systems such as Hadoop, Spark etc." We'll put those as

• . Now responsibilities: We need to capture bullet points. The responsibilities are in paragraph sentences. We can turn each sentence into
• . But we need to preserve content. Could group into
  with each responsibility as
  • . Let's extract bullet-like items: - See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. - Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. - Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. - Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. - Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. - Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. - Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. - A day in the life: We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. Error detection and price quality guardrails at scale. Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods). Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. We could break into multiple
  • . Ensure we keep all details. Now About the role: We'll put the initial paragraph(s) as

    . Might also include team description. Let's craft About the role paragraph: combine the initial description and team description. Initial description: "We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment." Team description: "The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment." We'll combine into one or two

    . Now Benefits: list items. Benefits: 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, parental leave. Also sign-on payments and RSUs mentioned earlier under Pay? Actually sign-on payments and RSUs are part of compensation, could be in Pay. We'll include them in Pay. Pay: base salary range: "USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually". Also sign-on payments and restricted stock units (RSUs). We'll include that. Now ensure we don't include any boilerplate like "Amazon is an equal opportunity employer..." etc. Now produce HTML fragment:

    About the role

    ...

    Responsibilities

    • ...
    ...

    Requirements

    • 4+ years of applied research experience
    ...

    Qualifications

    • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
    • Experience with large scale distributed systems such as Hadoop, Spark etc.

    Benefits

    • 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 Sur