Lennar is looking to design, build, and deploy advanced models and AI agents to optimize pricing, sales, operations, and customer engagement across its 40+ divisions, aiming to improve sales velocity, revenue, and customer personalization.
Requirements
- Strong proficiency in Python and SQL, with experience owning the full data science stack (data pipelines + models + deployment).
- Experience with pricing optimization, revenue management, economic modeling, and price elasticity/demand modeling.
- Experience building and deploying large-scale recommender systems (collaborative filtering, embeddings, contextual bandits).
- Hands-on experience with AI development frameworks (LangChain, Strands, Amazon Bedrock, AgentCore, or equivalent).
- Experience with experimentation frameworks (A/B testing, uplift modeling, multi-armed bandits, causal ML).
- Exposure to machine vision techniques (CNNs, transfer learning, embeddings) and NLP techniques (embeddings, transformers, prompt engineering).
- Familiarity with real-time or near-real-time systems (Kafka, Kinesis, Flink, or similar) for scalable personalization.
Responsibilities
- Design, build, and deploy pricing recommendation models to optimize sales velocity, revenue, and division-level targets.
- Develop sales forecasting and demand prediction models to support pricing and inventory decisions.
- Build personalization algorithms for tailored product recommendations and communications across email, text, and digital platforms.
- Apply machine vision and feature extraction on home attributes (photos, plans, finishes) to inform premium pricing and personalization strategies.
- Design, build, and deploy autonomous AI agents using frameworks like Amazon Bedrock and AgentCore to solve business problems in pricing, sales, operations, and customer interactions.
- Engineer and maintain data pipelines and systems supporting all models and agents, ensuring scalability and reliability.
- Integrate agents with enterprise systems and protocols (MCP servers, A2A protocol, internal APIs).
Other
- 5+ years of relevant experience (1+ with PhD, 3+ with MS) as a data scientist, ML engineer, or applied AI developer delivering production-ready models and systems.
- Understanding of AI agent observability (evaluation frameworks like LangFuse, RAGAS, Weights & Biases, custom monitoring).
- Experience with system integrations: APIs, A2A protocol, MCP servers, orchestration pipelines.
- Comfort working with large-scale, imperfect real-world datasets and making progress despite complexity.
- Strong engineering skills: ability to design and maintain production pipelines, microservices, and scalable systems.
