Machine Learning Engineer II

We need to output HTML fragment with sections only if content present. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content. The posting includes description: first paragraph about role. Then "Core Accountabilities" with bullet-like paragraphs. Then sections: Product Model & Discovery Partnership, Machine Learning, Data Platform & Segmentation Architecture, Engineering Execution & Data Quality, Individual Contributor Technical Leadership, Microsoft Azure Data Platform & Fabric Expertise, Business Partnership & Communication, Governance, Privacy & Compliance, Success Measures, Leadership Profile, Required Experience & Capabilities. We need to decide which sections to output: About the role (intro paragraph). Responsibilities (core accountabilities). Requirements (maybe Required Experience & Capabilities). Qualifications (maybe education). Skills (maybe technical expertise). Benefits, Pay, Schedule not present -> omit. We must not repeat job title as heading. Use

for each section heading. We need to preserve all substantive info, not invent. Use

• for any list of duties/requirements/benefits. The posting has paragraphs, not explicit bullet lists. We can convert each bullet-like sentence into
• . But we must not invent list where not present? The instruction: "Use
  • for any list of duties/requirements/benefits". If the content is not already a list, we could still wrap each duty in
  • inside a
    . That seems acceptable. We need to avoid filler like "commensurate with experience". Not present. Let's extract: About the role: first paragraph: "The Senior Manager, Software Engineer, Data Platform & Segmentation is a senior individual contributor accountable for the technical vision, design, and evolution of data platforms and segmentation capabilities that power Customer and Commercial product teams operating under a modern Product Operating Model. This role functions as a hands-on technical leader and multiplier, shaping how customer, commercial, and behavioral data is modeled, segmented, and activated across products, enabling better decisions, personalization, and measurable business outcomes. The role emphasizes deep technical expertise, product partnership, and architectural leadership, rather than people management." Responsibilities: core accountabilities sections. We can group them under Responsibilities. List each bullet: - Product Model & Discovery Partnership: Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets. Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound. Translate business and customer questions into durable data models and segmentation frameworks. - Machine Learning: Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies. Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products. Collaborating with data scientists managing standards for model life cycle, monitoring drift, retraining, etc. Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines. - Data Platform & Segmentation Architecture: Define and evolve the segmentation architecture across customer and commercial data domains. Design scalable data models that support real-time, near real time, and batch segmentation use cases. Ensure segmentation logic is reusable, explainable, and consistent across channels and products. Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability. - Engineering Execution & Data Quality: Build and maintain high-quality, production-grade data pipelines and services. Ensure strong standards for data quality, lineage, observability, and reliability. Reduce fragmentation and duplication in segmentation logic across teams. Leverage metrics to continuously improve data freshness, accuracy, and usability. - Individual Contributor Technical Leadership: Act as a go-to expert for data platform and segmentation design. Lead complex technical initiatives end-to-end through hands-on contribution. Influence technical direction through design reviews, reference implementations, and documented standards. Mentor senior engineers and Tech Leads through coaching and technical guidance (without direct management responsibility). - Microsoft Azure Data Platform & Fabric Expertise: Demonstrate deep, hands-on expertise with Microsoft Azure data services and their application in large-scale, product-centric environments. Design and evolve segmentation and data platform architectures leveraging Azure Data Fabric concepts, ensuring interoperability, governance, and reuse across domains. Apply strong architectural judgment across core Azure data products, including data ingestion, storage, processing, analytics, and activation layers. Optimize designs across cost, performance, latency, and scalability, using Azure-native capabilities and patterns. Ensure secure-by-design implementations aligned with Azure identity, access, encryption, and compliance controls. Partner with enterprise architecture, cloud, and security teams to ensure Azure data platform decisions align with broader enterprise strategy while preserving team autonomy. Stay current on Azure data platform evolution and proactively assess new capabilities for business value, not novelty. - Business Partnership & Communication: Serve as a trusted technical partner to Customer and Commercial stakeholders. Communicate segmentation concepts, assumptions, and limitations in clear business language. Proactively surface data constraints, privacy considerations, and trade-offs to enable informed decisions. Support external partner and vendor conversations as a technical authority when needed. - Governance, Privacy & Compliance: Ensure segmentation approaches comply with data privacy, consent, and regulatory requirements. Collaborate with Security, Privacy, and Legal teams to embed governance into platform design—not bolt it on later. Advocate for responsible and ethical use of customer and commercial data. - Success Measures: Segmentation capabilities measurably improve customer engagement and commercial outcomes. Reduced duplication and inconsistency in segmentation logic across products. Improved data quality, freshness, and trustworthiness. Faster time-to-insight and activation for product teams. Platforms and models that scale with growth while controlling cost and risk. - Leadership Profile: Outcome-driven, not data for data’s sake. Deep technical expertise with strong product intuition. Influences through credibility and clarity, not authority. Comfortable operating in ambiguity and evolving problem spaces. Holds a high bar for data quality, ethics, and reliability. - Required Experience & Capabilities: Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience. 8+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles. Deep expertise in data modeling, segmentation strategies, and large-scale data systems. Strong experience with cloud-native data platforms and modern data tooling. Proven ability to partner closely with product and business stakeholders. Demonstrated impact as a senior individual contributor on complex, cross-team initiatives. We need to map to sections: About the role (intro). Responsibilities (core accountabilities). Requirements (maybe Required Experience & Capabilities). Qualifications (education). Skills (technical expertise). Could also have a separate section for "Leadership Profile" maybe as Skills? But we only allowed headings list: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we must fit content into those headings only. We cannot add extra headings like "Success Measures" etc. We need to incorporate all substantive info under those allowed headings. Possibly we can put Success Measures under Responsibilities or Skills? But we must not invent headings not allowed. The instruction: "use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It says e.g., meaning those are examples, but we should use those headings when appropriate. It also says "include a section ONLY when the posting has real, specific content for it — never emit a heading followed by filler/placeholder text". So we can use any of those headings if content matches. We could also use other headings? The examples given are those, but not explicitly forbidden to use others. However safer to stick to those. We need to decide mapping: - About the role: intro paragraph. - Responsibilities: core accountabilities (all the bullet sections). Could also include Success Measures? Might be better as part of Responsibilities or maybe Skills. - Requirements: Required Experience & Capabilities (experience, education). Could also include Leadership Profile? Might be more like Skills. - Qualifications: education (Bachelor's degree). Could be part of Requirements. - Skills: technical expertise (Machine Learning, Azure, Data Platform, etc.) plus Leadership Profile traits. We must not duplicate content. Let's try to distribute: About the role: intro. Responsibilities: list of accountabilities (Product Model & Discovery Partnership, Machine Learning, Data Platform & Segmentation Architecture, Engineering Execution & Data Quality, Individual Contributor Technical Leadership, Microsoft Azure Data Platform & Fabric Expertise, Business Partnership & Communication, Governance, Privacy & Compliance). That's a lot; we can put each as

    . Requirements: Required Experience & Capabilities (8+ years experience, deep expertise, strong experience with cloud-native data platforms, proven ability to partner, demonstrated impact). Also maybe include Bachelor's degree? But that's qualification. Qualifications: Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience. Skills: Machine Learning expertise, Azure Data Platform & Fabric expertise, data modeling, segmentation strategies, large-scale data systems, cloud-native data platforms, modern data tooling, product partnership, influencing, mentoring, leadership profile traits (outcome-driven, technical expertise, product intuition, influencing through credibility, comfort with ambiguity, high bar for data quality, ethics, reliability). Also maybe include Success Measures? Could be under Skills? But success measures are outcomes, not skills. Could be under Responsibilities as impact metrics. Might be okay to put under Responsibilities as bullet. We need to ensure we don't leave any content out. Let's list all content pieces and assign. Content list: 1. Intro paragraph (About the role). 2. Product Model & Discovery Partnership (3 sentences). 3. Machine Learning (4 sentences). 4. Data Platform & Segmentation Architecture (4 sentences). 5. Engineering Execution & Data Quality (4 sentences). 6. Individual Contributor Technical Leadership (4 sentences). 7. Microsoft Azure Data Platform & Fabric Expertise (many sentences ~9). 8. Business Partnership & Communication (3 sentences). 9. Governance, Privacy & Compliance (3 sentences). 10. Success Measures (5 bullet points). 11. Leadership Profile (5 bullet points). 12. Required Experience & Capabilities (6 bullet points). We need to ensure all are included. We can put Success Measures under Responsibilities as "Impact/Success Measures". Or maybe under a separate heading but not allowed. We'll put under Responsibilities as additional items. Leadership Profile could go under Skills (as soft skills) or maybe under Responsibilities? It's more about personal attributes. We'll put under Skills. Now craft HTML. We'll have:

    About the role

    ...

    Responsibilities

    • Product Model & Discovery Partnership: Partner closely with Product Managers, Designers, and Tech Leads to co-own outcomes, not just data assets. Participate actively in product discovery to ensure segmentation strategies are technically feasible, scalable, and analytically sound. Translate business and customer questions into durable data models and segmentation frameworks.
    • Machine Learning: Strong experience in Machine Learning engineering, leveraging ML models to build Segmentation strategies. Experience operationalizing ML-driven segmentation, including integrating segmentation outputs into other products. Collaborating with data scientists managing standards for model life cycle, monitoring drift, retraining, etc. Understanding of Azure ML (or any other cloud) workspaces and integrating them with Azure pipelines.
    • Data Platform & Segmentation Architecture: Define and evolve the segmentation architecture across customer and commercial data domains. Design scalable data models that support real-time, near real time, and batch segmentation use cases. Ensure segmentation logic is reusable, explainable, and consistent across channels and products. Make explicit trade-offs across latency, accuracy, cost, privacy, and maintainability.
    • Engineering Execution & Data Quality: Build and maintain high-quality, production-grade data pipelines and services. Ensure strong standards for data quality, lineage, observability, and reliability. Reduce fragmentation and duplication in segmentation logic across teams. Leverage metrics to continuously improve data freshness, accuracy, and usability.
    • Individual Contributor Technical Leadership: Act as a go-to expert for data platform and segmentation design. Lead complex technical initiatives end-to-end through hands-on contribution. Influence technical direction through design reviews, reference implementations, and documented standards. Mentor senior engineers and Tech Leads through coaching and