Pay Range: The pay range for this job is $93500 - $177590 annually; actual pay is dependent on experience and geographic location.
Benefits: Base pay is only one part of our employee value proposition, which includes a robust benefits package, above-market time off, hybrid working arrangements, incentive compensation, where applicable, and varied learning opportunities.
COMPANY DESCRIPTION
Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands.
At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.
POSITION OVERVIEW
Position Overview - Build and lead the MLOps, standards and governance capability for Decision Science, creating a consistent, controlled and scalable path from analytical development to production across global markets and functions. The role will establish practical governance across Data Science and MLOps - covering model development standards, validation, documentation, approvals, lineage, monitoring, exceptions and lifecycle controls - while partnering closely with Data Products and Engineering to industrialise solutions on Databricks. Governance should enable speed, trust and reuse, not become a bureaucratic gate.
ESSENTIAL DUTIES & RESPONSIBILITIES
- MLOps strategy and operating model for Decision Science, including deployment, model registry, monitoring, retraining, versioning and retirement.
- Enterprise model-development and production standards covering reproducibility, documentation, testing, approval gates, model risk and responsible use.
- Databricks / MLflow-based production patterns that are reusable across central, market and functional teams.
- Clear interfaces with Data Products and Engineering for data readiness, production pipelines, platform dependencies, release management and run ownership.
- Model observability - performance, drift, failures, data-quality signals, usage and cost - with actionable operational dashboards and escalation paths.
- A governed pathway for models built outside the central team to enter production without creating fragmented local patterns.
- Capability build-out for the MLOps team, including hiring, vendor/partner management, coaching and technical quality.
- Technical coordination of major platform transitions affecting Decision Science, including the move from Dataiku to Databricks, in partnership with the wider D&A organisation.
Governance mandate across Data Science & MLOps
- Define a risk-based governance framework for analytical and ML solutions, with proportionate controls based on business impact, model complexity and production criticality.
- Set mandatory development standards for problem definition, data lineage, feature logic, experimentation, validation, peer review, reproducibility and documentation.
- Establish stage gates from prototype to production, including model readiness, business sign-off, technical validation, monitoring design and rollback readiness.
- Maintain a central model and analytical asset inventory with clear ownership, versioning, dependencies, deployment status, monitoring status and retirement decisions.
- Create an exception-management process so deviations from standards are visible, time-bound, approved and remediated rather than becoming permanent local workarounds.
- Establish ongoing governance after go-live: performance thresholds, drift, data quality, usage, cost, incidents, retraining triggers and periodic model health reviews.
- Partner with Data Products, Engineering, Security, Architecture and business owners to define decision rights and evidence required at each control point.
- Build governance as reusable templates, automated checks and platform controls wherever possible, reducing manual review and accelerating compliant delivery.
EXPERIENCE, SKILLS & KNOWLEDGE
Skills - Required experience and technical depth
- 10+ years of experience across machine learning engineering, MLOps, data/ML platforms or related engineering disciplines, with meaningful leadership experience.
- Hands-on understanding of end-to-end ML lifecycle design: experiment tracking, model registry, CI/CD/CT, deployment, monitoring, drift, retraining and retirement.
- Strong experience with Databricks and MLflow; good understanding of cloud-native data/ML architectures, APIs, containers and orchestration patterns.
- Experience designing production-grade observability for ML systems, including performance, data quality, drift, failures and operational metrics.
- Working knowledge of GenAI / LLMOps patterns such as evaluation, prompt/model versioning, monitoring and guardrails is desirable.
- Experience operating in a complex enterprise with multiple business units, markets or federated teams.
- Demonstrated ability to define standards and drive adoption through influence - not just publish documentation.
- Strong partnership skills with Data Products, Engineering, Architecture, Security and business stakeholders.
Governance experience
- Experience establishing model or analytics governance in an enterprise environment, including model inventory, validation, approvals, monitoring and exception management.
- Strong understanding of model risk, reproducibility, lineage, auditability, responsible AI and controls across both traditional ML and GenAI/LLMOps.
- Ability to translate governance requirements into engineering patterns, automated checks and platform controls rather than manual process alone.
- Track record of influencing Data Scientists and Engineers to adopt standards through practical tooling, templates and clear decision rights.
Leadership profile
- A builder who can create the capability and operating model, not just run an existing platform.
- Technically credible enough to challenge design decisions and coach engineers, while remaining outcome-oriented.
- Comfortable navigating ambiguity, competing priorities and global stakeholder complexity.
- Able to balance standardisation with pragmatism - knowing where consistency matters and where variation is justified.
- Strong communicator who can translate engineering choices into business, risk, cost and scalability implications
Ralph Lauren will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the requirements of the law, including any applicable fair chance laws. Please note background checks will be evaluated individually.
