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Bloomingdale's
Data Scientist Lead
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What they do
A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
$116,614 / year median in New York
+10% projected growth
Job Description
About Bloomingdale's makes fashion personal and fun, aspirational yet approachable. Our mission is to guide and inspire our customers to make style a source of creative energy in their lives. We will always strive to make Bloomingdale's like no other store in the world. Across all brand touchpoints—from Bloomingdales.com to our newest small store concept, Bloomie's—everyone plays a critical role bringing our mission to life. Our inclusive culture promotes diversity of background, thought and opinion. Regardless of position, we believe all colleagues have a voice and access to share their thoughts with every level of leadership. Our colleagues are passionate, driven, entrepreneurial and collaborative, while having a lot of fun along the way. Job Overview The Lead, Data Scientist will design, develop, and implement advanced data science models for priority business use cases across the enterprise, such as online experience, marketing, merchandising, supply chain, finance, and store operations. The Lead Data Scientist will work in the areas of feature engineering, deep learning, data insights and analytics. The Lead Data Scientist will build new AI enabled smart services that surprise and delight our customers and work with big data (text, images, audio & other) to solve real-world problems. Essential Functions Thought leader in data science and analytics who can help the business define their business problem, create solutions to address it, plan and execute the implementation. Collaborate with business stakeholders to define business requirements including KPI and acceptance criteria. Lead research initiatives into state-of-the-art methodologies that will enhance current models and power future personalized models. Collaborate with data engineers, ML engineers and Data Scientists in building real-time and batch machine learning pipelines that include data preprocessing, feature engineering, model training, model validation, serving, and evaluating results of A/B test.