Principal, Data Scientist - Digital + Customer

Be part of an amazing story.

Macy’s is more than just a store. We’re a story. One that’s captured the hearts and minds of America for more than 160 years. A story about innovations and traditions…about inspiring stores and irresistible products…about the excitement of the Macy’s 4th of July Fireworks, and the wonder of the Thanksgiving Day Parade. We’ve been part of memorable moments and milestones for countless customers and colleagues. Those stories are part of what makes this such a special place to work.

 

Job Overview

The Principal, Data Scientist plays a key role in advancing Macy’s mission to become a data-led, customer-centric company. As a member of the Enterprise Data & Analytics team, they help accelerate the impact of analytics, support the coordination of an enterprise-wide roadmap, and ensure effective data governance, engineering, and management.

As part of the enterprise Machine Learning team, the Principal, Data Scientist executes high-impact projects that build profitable, long-term customer relationships by embedding data and analytics into every aspect of the business. They lead the design, development, and implementation of advanced data science models and machine learning applications for critical business use cases across the enterprise—including online experience, marketing, merchandising, supply chain, finance, and store operations.

They apply cutting-edge data science techniques to large datasets to extract actionable insights and develop scalable, intelligent AI solutions. In doing so, they help productionize analytics and algorithms, establish data-driven norms and standards, and contribute to a culture of innovation and analytical excellence across the organization.

 

What You Will Do

• Execute advanced analytics using scalable, reusable code and models that effectively solve business problems.
• Collaborate closely with business unit partners to generate and test hypotheses aligned to priority use cases.
• Adhere to analytics standards, including tailored methodologies based on use case needs (e.g., machine learning, AI, descriptive analytics).
• Define data needs for batch and real-time streaming use cases, assess data quality, and extract/manipulate data within a big data environment.
• Identify internal and external data sources, assess quality, and determine suitability for specific analytical objectives.
• Develop and validate predictive models to support key business initiatives.
• Create impactful visualizations to clearly communicate insights derived from data.
• Partner with Data and Solution Architecture teams to implement data pipelines and tools that enable efficient execution of analytics.
• Work with Data Engineering teams to develop enterprise-wide data assets that accelerate use case delivery and enhance machine learning model training.
• Collaborate with Technology teams to scale and roll out analytics solutions across the organization.
• Manage multiple analytics projects, tracking activities and deliverables to ensure timely execution.
• Provide technical guidance and mentorship to Lead Data Scientists in partnership with the Senior Director.
• Demonstrate technical excellence by continuously enhancing subject matter expertise and staying current on analytical tools, techniques, and practices.
• Champion a data-driven culture by increasing awareness, knowledge, and adoption of analytics across business units.
• Proactively identify data issues and take swift action to resolve them.
• Foster an environment of acceptance and respect that strengthens relationships, and ensures authentic connections with colleagues, customers, and communities. 
• In addition to the essential duties mentioned above, other duties may be assigned.

 

Skills You Will Need

Advanced Analytics & Machine Learning Expertise: Proficiency in developing predictive models, segmentation, regression, clustering, time-series analysis, and machine learning techniques.

Big Data Proficiency: Ability to define and manipulate large-scale batch and real-time streaming data in a big data environment.

Programming & Tools: Fluency in analytics languages and platforms such as Python, R, SQL, and Google Cloud Platform (GCP), with scalable, reusable coding practices.

Data Engineering Collaboration: Experience working with archit