Data Science Manager, Forecasting & Optimization

New Balance•Boston, MA

12d•Hybrid

About The Position

The Data Science Team at New Balance is the center of excellence for applied data science across our global commercial businesses, creating value through actionable insights, scalable models, and AI-enabled decision support. As the Data Science Manager, Forecasting & Optimization, you will lead New Balance’s planning-focused data science group and own high-value forecasting and optimization work that improves global planning, inventory, and supply chain decisions. This is a hands-on player/coach role where you will write production-quality code, develop and deploy models, lead and develop data scientists and contractors, partner with stakeholders, and ensure solutions are adopted, monitored, and improved.

Requirements

6+ years of experience in data science, forecasting, optimization, operations research, or a related field, including hands-on model development and deployment.
2+ years leading projects and/or guiding cross-functional delivery while remaining hands-on with technical work.
2+ years leading data scientists
Deep expertise with forecasting models and optimization techniques across areas such as demand planning, inventory optimization, simulation, or decision science, ideally in a retail environment.
Strong Python and SQL skills with experience building scalable, production-quality modeling workflows using large datasets.
Ability to partner with senior stakeholders, define roadmaps, influence priorities, communicate clearly, and drive adoption of analytical or AI-enabled solutions.
Strong understanding of model evaluation, monitoring, retraining, governance, documentation, and lifecycle management.
BA/BS in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, Operations Research, or a related technical field; Master's or PhD preferred.

Nice To Haves

Practical data science experience in retail, consumer products, or supply chain is preferred.
Experience with Databricks, Snowflake, Azure, AWS, GCP or similar cloud platforms is preferred.
Experience building production data science products, APIs, dashboards, optimization engines, or decision-support tools.
Familiarity with Agile delivery, product ownership, roadmap planning, and measurable adoption strategies.

Responsibilities

Develop, deploy, and improve forecasting and optimization models for demand planning, allocation, assortment, inventory, and related planning decisions.
Write, review, and maintain high-quality Python and SQL code that can scale from experimentation into reliable production workflows.
Connect model performance metrics such as accuracy, bias, stability, and service levels to business outcomes including sales, inventory productivity, margin, and efficiency.
Partner with stakeholders to define priorities, align roadmaps, and translate business needs into scalable data science products.
Document model logic, business rules, assumptions, and operating practices so solutions can scale and be supported over time.
Work with ML engineering and MLOps partners to make models production-ready, monitored, explainable, and maintainable.
Manage and coach a small team (1-3) of data scientists while staying hands-on in model development, code reviews, technical design, and production delivery.
Create repeatable standards for experimentation, validation, governance, documentation, stakeholder communication, and adoption measurement.
Build a culture of technical excellence, pragmatic problem-solving, collaboration and stakeholder empathy, and continuous learning.