Sr Manager, Data Science

Lennar•Waterford, FL

6d•Hybrid

About The Position

We are Lennar 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. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States. Join a Company that Empowers you to Build your Future Lennar is seeking a Senior Manager of Data Science to lead our centralized DS team within the Applied AI & Data Science function. This role owns the strategy, delivery, and people leadership of a high-impact team building pricing, forecasting, and predictive models that influence revenue, operations, and capital decisions across the business. The ideal candidate is a hands-on technical leader who can move fluidly between coaching senior data scientists, shaping modeling roadmaps with executive stakeholders, and reviewing the math behind a model when it matters. They bring deep applied ML experience—across pricing, forecasting, supervised learning, and modern ML tooling—and they know how to translate ambiguous business problems into production-grade models that move metrics. They build teams that ship, document, and own outcomes. You’ll join a high-performing Data & AI organization operating at the intersection of real estate, operations, and AI—leading a team whose models are deployed across 40+ divisions of one of the nation’s largest homebuilders. A career with purpose. A career built on making dreams come true. A career built on building zero defect homes, cost management, and adherence to schedules.

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.

Nice To Haves

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

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.