Lead Machine Learning Engineer

Allergan Data Labs is on a mission to transform the Allergan Aesthetics beauty business at AbbVie, one of the largest pharmaceutical companies in the world. Our iconic brands include BOTOX® Cosmetic, CoolSculpting®, JUVÉDERM® and more. The medical aesthetics business is ripe for rapid growth and disruption, and we are looking to add to our high performing team to do just that. 

Our team has successfully launched a new and innovative technology platform, Allē, which serves millions of consumers, tens of thousands of aesthetics providers and thousands of colleagues throughout the US. Since its launch in November 2020, Allē has delivered curated promotions, personalized experiences and had millions of consumers use it as part of their beauty journey. 

We’re looking to add to our team as we prepare to launch a new array of game-changing technologies on our successfully adopted platform. If you’re interested in working within a startup-oriented environment, while having the backing of a very large company, please read on.

Allergan Data Labs is a vibrant startup-minded organization with the backing of a large company. As a Lead Machine Learning Engineer, you will be responsible for collaborating with cross functional partners and applying your Machine Learning Engineering skills to deliver data-driven solutions for product teams, operations, marketing, and sales.

Responsibilities

• Take ownership for achieving objectives and key results for your team, allocate resources, oversee & own technical solutions, communicate schedule, status, and milestones

• Lead and manage a small team of Machine Learning Engineers by setting goals, supervising work, providing guidance, evaluating performance, removing barriers, cultivating career development, and promoting job satisfaction

• Collaborate with cross functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data products

• Architect and build robust systems to train, deploy, run inference, and monitor machine learning and AI models at scale

• Champion code quality, reusability, scalability, maintainability, and security as well as providing input for strategic architecture decisions

• Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices

• Integrate Machine Learning and AI systems with production applications

• Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community

Required Experience & Skills

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