Senior Manager, Engineering - Data Science

Position Title: Sr Manager, Engineering - Data Science
Location: Remote from the U.S. 
Reports to: CPTO

About the role

We are hiring a Senior Data Science Manager to lead the team building LTK’s foundational AI capabilities. This team develops shared models, services, tools, and evaluation frameworks that enable teams across LTK to build and deploy AI effectively.

You will help define the AI platform roadmap, identify capabilities that can serve multiple teams, and turn promising approaches into reliable production services. The work spans areas such as embeddings, semantic understanding, retrieval, model evaluation, and access to large language models, supporting applications across search, recommendations, catalog, and other product and business workflows.

We are looking for a technically strong people leader who can connect AI with practical platform development. You will partner closely with engineering and product teams to make shared AI capabilities easy to adopt, operate, and improve. Success means helping those teams deliver better experiences faster, with measurable quality, reliability, and cost efficiency.

Building a high-performing team is central to this role. You will set clear expectations, provide direct feedback, coach team members, and manage performance.

 

How you will make an impact

• Build shared AI capabilities. Lead a team developing reusable models, services, and tools that address common needs across LTK.
• Shape the platform roadmap. Partner with engineering, product, and data leaders to prioritize foundational investments based on business value, demand across teams, and technical dependencies.
• Enable other teams. Work with internal partners to understand their needs, support integration, and provide clear documentation and technical guidance.
• Deliver production-ready systems. Partner with engineering on deployment, monitoring, versioning, and ongoing operation of AI services.
• Make evals foundational to AI development. Own the team’s approach to evaluating AI systems, from shared evaluation tooling and representative datasets to release criteria and production quality monitoring. Enable partner teams to measure whether AI capabilities work reliably for their use cases and improve them with evidence.
• Manage quality and cost. Guide decisions about models, providers, and architecture, balancing accuracy, latency, reliability, and operating cost.
• Own execution and outcomes. Set clear priorities, manage dependencies, and measure success through platform adoption, service performance, and the outcomes enabled for partner teams.
• Develop the team. Hire and retain strong talent, support career growth, give regular feedback, and address performance issues directly.

 

What you will bring to LTK

Data Science and AI

• 7+ years of experience in data science or machine learning
• 3+ years of direct people management experience leading data science or machine learning teams, including hiring, coaching, career development, and performance management.
• Experience shipping AI systems. You have delivered LLM-powered capabilities into production and owned their performance after launch, including quality, reliability, latency, and cost. Your experience includes operating AI systems beyond prototypes and traditional ML projects.
• Strong experience building and operating AI evaluations (evals). You have created representative evaluation datasets, defined task-specific quality criteria, and built automated evaluations with human review where needed. You have used evals to guide model and prompt selection, catch regressions, establish release criteria, and monitor quality in production.
• Experience building shared AI capabilities. You have built AI services, tools, or platforms that multiple teams have adopted and used in production. You understand how to make those capabilities reusable, reliable, and easy to integrate.
• Strong technical foundations. You bring a solid grounding in machine learning, applied statistics, and experimentation. You use that knowledge to choose practical approaches, test assumptions, and measure results.

Platform and Technical Leadership

• Platform strategy and ownership. You can translate needs across teams into a focused AI platform roadmap. You prioritize investments that solve shared problems, accelerate delivery, and create lasting value.
• Production service leadership. You have partnered with engineering to deliver reliable AI services, shaping service interfaces, deployment practices, observability, and operational readiness. You establish clear ownership and measurable expectations for quality, latency, availability, and cost.
• Adoption and developer experience. You treat internal teams as customers. You make shared capabilities easy to discover, integrate, and operate through clear interfaces, documentation, and practical support, and use adoption and feedback to guide improvements.
• Sound architectural judgment. You make informed decisions about what to build, buy, or adopt. You evaluate models, providers, and tools against demonstrated quality, integration effort, operating cost, and long-term maintainability.
• Hands-on technical leadership. You stay close enough to the work to review code and architecture, challenge assumptions, and resolve difficult technical problems. You set clear standards while developing your team’s judgment and ownership.

A bachelor’s or master’s degree in Computer Science, Statistics, Data Science, Engineering, or a related field is preferred. Equivalent practical experience is equally valued.

Nice to have

• Experience with large-scale or real-time data and inference systems.
• Experience with retrieval-augmented generation or AI workflow orchestration.
• Experience in marketplaces, social commerce, or recommendation-driven products.

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