Lead Machine Learning Engineer - Merchandising AI (ML Ops)

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here .

A role with Target Data Sciences means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Applied Data Sciences or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Digital Marketing, Supply Chain Optimization, Advanced AI, Search and Personalization rely on. Every Scientist on Target’s Data Sciences team can expect modeling and data science, software/product development of highly performant code for model performance at scale.

As the Lead Machine Learning Engineer – Merchandising, you will join a Data Sciences team responsible for creating and optimizing the data and models used to build a world-class merchandising product. You will play a crucial role in designing, implementing, and optimizing production machine learning solutions. We will also expect you to understand best practice software design, participate in code reviews, and create a maintainable well-tested codebase with relevant documentation. At an organizational level, you may conduct training sessions, present work to technical and non-technical peers/leaders, build knowledge on business priorities/strategic goals and leverage this knowledge while building requirements and solutions for each business need.

Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience

MS in Computer Science, Applied Mathematics, Statistics, Physics or equivalent work or industry experience in applied machine learning

5 plus years of experience in end-to-end machine learning application development, including data pipelining, model optimization, deployment and API design