Sr Manager, Data Science

About the role

Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live in, and fostering a culture of opportunity and growth for our Associates throughout their career.

Responsibilities

• Lead, coach, and grow a centralized team of data scientists working across pricing, forecasting, demand modeling, and broader predictive analytics—setting standards for technical rigor, code quality, and business impact.
• Own the modeling roadmap for the ML team, partnering with business and platform leaders to prioritize use cases, scope deliverables, and align modeling investments to measurable enterprise outcomes.
• Drive technical depth across the team—reviewing experiment design, feature engineering, model selection, validation strategy, and post-deployment monitoring with the rigor expected of a hands-on senior practitioner.
• Partner with AI Engineering, AI Product, and Data Engineering counterparts to ensure models are productionized on a modern MLOps stack with proper version control, retraining, and observability.
• Translate complex modeling work for executive audiences—framing tradeoffs, expected impact, confidence levels, and risks in language that supports clear decision-making.
• Build durable cross-functional partnerships with Pricing, Sales Operations, Supply Chain, Finance, and Corporate Analytics to ensure models are adopted, trusted, and tied to measurable business outcomes.
• Establish team operating cadence including planning, retros, model reviews, and documentation standards that scale as the team and portfolio of models grow.
• Recruit, develop, and retain top data science talent—owning hiring loops, leveling decisions, performance management, and career progression for direct reports.

Requirements

• Bachelor’s degree or higher in a quantitative field such as Statistics, Computer Science, Operations Research, Economics, Mathematics, or Engineering. Advanced degree (MS/PhD) preferred.
• 10+ years of applied data science experience with at least 3 years in a people management role leading data science teams that shipped production models.
• Strong hands-on background in supervised learning, time-series forecasting, and pricing or revenue optimization models—with proven impact in a production environment.
• Deep proficiency in Python and the modern ML stack (scikit-learn, XGBoost/LightGBM, pandas), and strong SQL skills for working with large-scale warehouse data.
• Experience deploying models on a cloud-native ML platform (AWS SageMaker preferred) and partnering with engineering teams on MLOps practices including model registries, experiment tracking, and retraining pipelines.
• Proven ability to lead a portfolio of modeling work—prioritizing use cases by business value, managing competing stakeholder demands, and communicating tradeoffs clearly at the executive level.
• Track record of building, developing, and retaining strong data science talent, including mentoring senior individual contributors and growing first-line data scientists into leaders.
• Bonus: Experience in real estate, homebuilding, pricing, supply chain, or other operationally complex industries; experience with causal inference, reinforcement learning, or simulation modeling; exposure to LLM-based ML use cases.