Lead Data Scientist - Merchandising and Pricing

Bring Your Amazing Self to Work

At Macy's, Inc. we're on a mission to create a brighter future with bold representation for all. This is our Mission Every One. We know that each person here is unique. So we respect and invest in each individual to create growth, pride, and satisfaction. If we are able to bring our whole selves to work, it translates into a more abundant and wider array of ideas and energy for all to benefit from. Our success will be built on amazing colleagues, working together.

 

Job Overview

The Lead Data Scientist designs, develops, and implements advanced analytics models and machine learning solutions to support key business use cases and enhance decision-making in a dynamic retail environment. He takes ownership of innovative data science and operations optimization solutions across pricing and merchandising, provides actionable insights for business leaders, and collaborates with stakeholders to drive the adoption of these insights.

 

What You Will Do

• Develop and implement advanced analytics solutions using scalable, reusable code and models to address business challenges.
• Partner with Business Units to generate and test hypotheses aligned with priority use cases.
• Adhere to analytics standards, tailoring methodologies (e.g., ML, AI, descriptive analytics) to meet specific use case needs.
• Define and assess batch or real-time streaming data requirements, ensuring data quality and effective extraction in a Big Data environment.
• Identify internal and external data needs, establish quality measures, and evaluate suitability for use.
• Build predictive models to uncover opportunities that drive business impact.
• Create high-impact visualizations to effectively communicate data-driven insights.
• Collaborate with Data and Solution Architecture teams to implement efficient data pipelines and analytics tools.
• Work with Data Engineering teams to develop enterprise-wide data assets that accelerate analytics delivery and enhance machine learning models.
• Support Tech teams in scaling and deploying analytics applications.
• Foster a data-driven culture by promoting analytics adoption and facilitating discussions on priority use cases.
• Champion data-driven decision-making at Macy’s, increasing digital IQ across Business Units.
• Empower business users to leverage insights for maximum impact.
• Stay updated on emerging data science methodologies and best practices.
• Proactively identify and address data issues to ensure accuracy and reliability.
• Develop and deploy algorithms and methodologies for analytics use cases.
• Ensure consistent application of coding and methodology best practices.
• Establish metrics to evaluate model effectiveness in achieving business objectives.
• In addition to the essential duties mentioned above, other duties may be assigned.

 

Skills You Will Need

Advanced Analytics & Machine Learning – Expertise in developing and implementing predictive models, deep learning, and GenAI solutions.

Programming & Data Science Tools – Proficiency in Python, R, SQL, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

Cloud Computing – Hands-on experience with Google Cloud Platform (GCP) and familiarity with cloud-based analytics services.

Big Data Processing – Strong knowledge of distributed computing frameworks such as Spark, Hadoop, or Databricks.

Statistical & Mathematical Expertise – Deep understanding of regression, clustering, time-series analysis, and other analytical methods.

Data Engineering & Pipeline Development – Ability to define batch/real-time data needs, evaluate data quality, and build scalable pipelines in collaboration with Data Engineering teams.

Visualization & Storytelling – Experience creating compelling visualizations to communicate insights using tools like Tableau, Power BI, or Matplotlib.

Business Acumen – Ability to translate complex data science concepts into actionable business insights that support pricing, merchandising, and operational decisions.

Cross-Functional Collaboration – Experience working with business units, data engineers, and tech teams to implement data-driven solutions.

Communication & Storytelling – Excellent written and verbal communication skills, with the abil