Senior Manager, Machine Learning Engineer

The Senior Manager, Software Engineer, Data Platform & Segmentation is a 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 engineer with 3 to 6 years of experience on data and machine learning engineering.
The role emphasizes deep technical expertise, product partnership, and architecture, rather than people management.
Core Accountabilities
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.

Data Platform & Segmentation Architecture

• Analyze and integrate structured and unstructured data from enterprise platforms, customers, and external data providers.
• Build scalable data preparation and feature engineering pipelines for ML applications.

Machine Learning

• Develop predictive and recommendation models using appropriate statistical and machine learning techniques.
• Evaluate and select appropriate approaches based on each use case, including: Classification and regression Ranking and recommendation Clustering and segmentation Time-series and forecasting Gradient-boosting and tree-based models Deep learning and transformers Computer vision Embeddings, vector search and RAG
• Build reliable training and inference pipelines for batch and near-real-time use cases.
• Develop APIs and services that expose model predictions to web, mobile, CRM, Salesforce, and other enterprise applications.
• Establish rigorous model evaluation, testing and validation practices.

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.
• Implement MLOps pipelines covering training, testing, versioning, deployment and model lifecycle management.
• Monitor production models for model performance, data quality, drift and other operational issues.
• Implement appropriate retraining, rollback and model versioning strategies.
• Troubleshoot issues across data pipelines, models, inference services, APIs, and production environments.
• Create reusable ML components and patterns that can support multiple Transaction Growth use cases.
• Participate in architecture reviews, code reviews and engineering design discussions.

Microsoft Azure Data Platform & Fabric Expertise

• 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.

Required Experience & Capabilities

• Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 3+ years of hands-on experience in data platform, analytics engineering, or backend engineering roles.
• Strong programming experience with Python or common data and ML libraries.
• Experience developing production machine learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, XGBoost, or equivalent.
• Strong understanding of supervised and unsupervised learning, model selection, feature engineering and statistical modeling.
• Experience preparing large datasets for machine learning, including cleansing, transformation, feature generation and quality validation.
• Strong SQL skills and experience working with large enterprise datasets.
• Experience designing training, evaluation and inference pipelines.
• Experience deploying machine learning models into production environments.
• Practical understanding of MLOps, including experiment tracking, model versioning, CI/CD, automated testing, deployment and monitoring.
• Experience developing or integrating APIs and services used for model inference.
• Strong software engineering practices including modular design, source control, code review, automated testing and production debugging.
• Experience working with cloud-based data and ML platforms.
• Ability to assess multiple modeling approaches and select the simplest solution capable of meeting the business objective.
• Strong communication skills and the ability to collaborate with product managers, business stakeholders, software engineers and data teams.