Sr Manager, Engineering - Data Science

LTK USA

2d•Remote

About The Position

LTK is hiring a Senior Data Science Manager to lead the team building foundational AI capabilities used across the company. The team develops shared models, services, tools, and evaluation frameworks that help other teams build and deploy AI effectively. The role will shape the AI platform roadmap, identify capabilities that serve multiple teams, and turn promising approaches into reliable production services. Areas of work include embeddings, semantic understanding, retrieval, model evaluation, and access to large language models for applications in search, recommendations, catalog, and other product and business workflows. The manager will partner closely with engineering, product, and data leaders to make AI capabilities practical, easy to adopt, operate, and improve. The role requires a technically strong people leader who can connect AI with platform development, guide measurable improvements in quality, reliability, and cost efficiency, and build a high-performing team through clear expectations, feedback, coaching, and performance management.

Requirements

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 delivering LLM-powered capabilities into production and owning their post-launch quality, reliability, latency, and cost.
Experience operating AI systems beyond prototypes and traditional machine learning projects.
Experience building and operating AI evaluations, including representative datasets, task-specific quality criteria, and automated evaluations with human review where appropriate.
Experience using evaluations to guide model and prompt selection, catch regressions, establish release criteria, and monitor production quality.
Experience building shared AI services, tools, or platforms adopted and used in production by multiple teams.
Strong grounding in machine learning, applied statistics, and experimentation.
Ability to translate cross-team needs into a focused AI platform roadmap and prioritize investments that solve shared problems and accelerate delivery.
Experience partnering with engineering to deliver reliable AI services, including service interfaces, deployment practices, observability, and operational readiness.
Ability to establish measurable expectations for service quality, latency, availability, and cost.
Ability to make shared capabilities discoverable and easy to integrate and operate through clear interfaces, documentation, and practical support.
Ability to make informed build, buy, or adopt decisions by evaluating quality, integration effort, operating cost, and maintainability.
Hands-on technical leadership, including reviewing code and architecture, challenging assumptions, and resolving technical problems.
Authorization to work in the U.S.; the posting states that work authorization is a precondition of employment and that new employment-visa sponsorships are not being offered.

Nice To Haves

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.
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.

Responsibilities

Lead a team developing reusable AI models, services, and tools that address common needs across LTK.
Partner with engineering, product, and data leaders to prioritize foundational AI platform investments based on business value, cross-team demand, and technical dependencies.
Work with internal partners to understand their needs, support integration, and provide documentation and technical guidance.
Partner with engineering on deployment, monitoring, versioning, and ongoing operation of production AI services.
Own the approach to AI evaluation, including shared evaluation tooling, representative datasets, release criteria, and production quality monitoring.
Help partner teams measure and improve the reliability of AI capabilities for their use cases.
Guide decisions about models, providers, and architecture, balancing accuracy, latency, reliability, and operating cost.
Set priorities, manage dependencies, and measure results through platform adoption, service performance, and outcomes for partner teams.
Hire and retain talent, support career growth, provide regular feedback, and address performance issues directly.
Review code and architecture, challenge assumptions, resolve difficult technical problems, and develop the team’s technical judgment and ownership.