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Engineering Manager, Machine Learning
- United States
- United States
À propos
DICK'S Sporting Goods , we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
If you are ready to make a difference as part of the world's greatest sports team, apply to join our team today!
OVERVIEW:
Join us as we transform technology, data, and analytics to build next-generation tools and platforms for athletes and teammates. As the Machine Learning Engineering Manager - Teamate, you will lead a highly skilled team, driving enterprise impact and shaping the future of a leading sports retailer. You will be at the forefront of merchandising improvement-delivering advanced machine learning and agentic solutions that optimize inventory, maximize assortment plans, and ensure the right products are available at the right time and place. You will collaborate closely with Product Management, Design and Architecture to ensure cross-functional alignment and deliver innovative software, while overseeing team development, talent management, and best practices to drive impactful results.
Key Responsibilities
Lead the development, deployment, and maintenance of scalable, reliable machine learning solutions for merchandising and inventory optimization. Define and guide technical strategy and architecture for enterprise-grade ML, and AI capabilities. Manage, mentor, and develop a high-performing team of machine learning engineers. Partner with product, business, and engineering leaders to translate goals into ML-driven solutions. Drive end-to-end delivery of ML solutions from discovery through production support. Lead transformation initiatives delivering ML and agentic solutions that improve inventory and assortment effectiveness. Champion agile methodologies and engineering best practices. Ensure high standards for quality, scalability, reliability, performance, and security. Design cloud-based ML deployment patterns including APIs, monitoring, and model lifecycle management. Align technical execution with business priorities and KPIs. Stay current with emerging technologies and recommend strategic improvements. Support portfolio management, budgeting, performance management, and information security. Foster a diverse, inclusive, and collaborative team environment. Lead systems-level design and integration, including APIs, event streams, and vendor-managed integrations. Plan and manage resources and lead onshore/offshore contractors. QUALIFICATIONS:
Bachelor's (Master's preferred) degree or equivalent experience in a quantitative field. 6+ years of experience in machine learning or related engineering fields. 2-3 years of experience in a technical leadership role. 1-3 years of people management experience. Experience with ML frameworks. Strong Python skills and experience with distributed data technologies such as Spark and Kafka. Experience designing and operating ML-powered APIs and production systems. Experience working in an Agile environment. Ability to influence senior and executive stakeholders. Retail technology experience required; merchandising or inventory planning experience is a plus. VIRTUAL REQUIREMENTS:
At DICK'S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments.
To ensure a smooth and secure experience, please note the following:
Cameras must be on
during all virtual interviews. AI tools are not permitted
to be used by the candidateduring any part of the interview process. Offers are contingent upon a
satisfactory background check
which may include
ID verification .
If you have any questions or need accommodations, we're here to help. Thanks for helping us keep the process fair and secure for everyone!
Compétences linguistiques
- English
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